data/validation.jsonl

{"input": "This paper presents an algorithm for computing optical flow , shape , motion , lighting , and albedo from an image sequence of a rigidly-moving Lambertian object under distant illumination .", "output": {"relations": {"used for": [{"head": {"text": "algorithm", "start": 23, "end": 32}, "tail": {"text": "computing optical flow , shape , motion , lighting , and albedo", "start": 37, "end": 100}}, {"head": {"text": "image sequence", "start": 109, "end": 123}, "tail": {"text": "algorithm", "start": 23, "end": 32}}], "feature of": [{"head": {"text": "rigidly-moving Lambertian object", "start": 129, "end": 161}, "tail": {"text": "image sequence", "start": 109, "end": 123}}, {"head": {"text": "distant illumination", "start": 168, "end": 188}, "tail": {"text": "rigidly-moving Lambertian object", "start": 129, "end": 161}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The problem is formulated in a manner that subsumes structure from motion , multi-view stereo , and photo-metric stereo as special cases .", "output": {"relations": {"conjunction": [{"head": {"text": "motion", "start": 67, "end": 73}, "tail": {"text": "multi-view stereo", "start": 76, "end": 93}}, {"head": {"text": "multi-view stereo", "start": 76, "end": 93}, "tail": {"text": "photo-metric stereo", "start": 100, "end": 119}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The algorithm utilizes both spatial and temporal intensity variation as cues : the former constrains flow and the latter constrains surface orientation ; combining both cues enables dense reconstruction of both textured and texture-less surfaces .", "output": {"relations": {"used for": [{"head": {"text": "spatial and temporal intensity variation", "start": 28, "end": 68}, "tail": {"text": "algorithm", "start": 4, "end": 13}}, {"head": {"text": "former", "start": 83, "end": 89}, "tail": {"text": "flow", "start": 101, "end": 105}}, {"head": {"text": "latter", "start": 114, "end": 120}, "tail": {"text": "surface orientation", "start": 132, "end": 151}}, {"head": {"text": "cues", "start": 72, "end": 76}, "tail": {"text": "dense reconstruction of both textured and texture-less surfaces", "start": 182, "end": 245}}], "hyponym of": [{"head": {"text": "former", "start": 83, "end": 89}, "tail": {"text": "cues", "start": 72, "end": 76}}, {"head": {"text": "latter", "start": 114, "end": 120}, "tail": {"text": "cues", "start": 72, "end": 76}}], "conjunction": [{"head": {"text": "former", "start": 83, "end": 89}, "tail": {"text": "latter", "start": 114, "end": 120}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The algorithm works by iteratively estimating affine camera parameters , illumination , shape , and albedo in an alternating fashion .", "output": {"relations": {"used for": [{"head": {"text": "estimating affine camera parameters , illumination , shape , and albedo", "start": 35, "end": 106}, "tail": {"text": "algorithm", "start": 4, "end": 13}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "An entity-oriented approach to restricted-domain parsing is proposed .", "output": {"relations": {"used for": [{"head": {"text": "entity-oriented approach", "start": 3, "end": 27}, "tail": {"text": "restricted-domain parsing", "start": 31, "end": 56}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Like semantic grammar , this allows easy exploitation of limited domain semantics .", "output": {"relations": {"used for": [{"head": {"text": "this", "start": 24, "end": 28}, "tail": {"text": "limited domain semantics", "start": 57, "end": 81}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In addition , it facilitates fragmentary recognition and the use of multiple parsing strategies , and so is particularly useful for robust recognition of extra-grammatical input .", "output": {"relations": {"used for": [{"head": {"text": "it", "start": 6, "end": 8}, "tail": {"text": "fragmentary recognition", "start": 29, "end": 52}}, {"head": {"text": "it", "start": 6, "end": 8}, "tail": {"text": "multiple parsing strategies", "start": 68, "end": 95}}, {"head": {"text": "multiple parsing strategies", "start": 68, "end": 95}, "tail": {"text": "recognition of extra-grammatical input", "start": 139, "end": 177}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Representative samples from an entity-oriented language definition are presented , along with a control structure for an entity-oriented parser , some parsing strategies that use the control structure , and worked examples of parses .", "output": {"relations": {"used for": [{"head": {"text": "control structure", "start": 96, "end": 113}, "tail": {"text": "entity-oriented parser", "start": 121, "end": 143}}, {"head": {"text": "control structure", "start": 96, "end": 113}, "tail": {"text": "parsing strategies", "start": 151, "end": 169}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "A parser incorporating the control structure and the parsing strategies is currently under implementation .", "output": {"relations": {"part of": [{"head": {"text": "control structure", "start": 27, "end": 44}, "tail": {"text": "parser", "start": 2, "end": 8}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper summarizes the formalism of Category Cooccurrence Restrictions -LRB- CCRs -RRB- and describes two parsing algorithms that interpret it .", "output": {"relations": {"used for": [{"head": {"text": "parsing algorithms", "start": 109, "end": 127}, "tail": {"text": "it", "start": 122, "end": 124}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The use of CCRs leads to syntactic descriptions formulated entirely with restrictive statements .", "output": {"relations": {"feature of": [{"head": {"text": "restrictive statements", "start": 73, "end": 95}, "tail": {"text": "syntactic descriptions", "start": 25, "end": 47}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The paper shows how conventional algorithms for the analysis of context free languages can be adapted to the CCR formalism .", "output": {"relations": {"used for": [{"head": {"text": "algorithms", "start": 33, "end": 43}, "tail": {"text": "CCR formalism", "start": 109, "end": 122}}, {"head": {"text": "context free languages", "start": 64, "end": 86}, "tail": {"text": "algorithms", "start": 33, "end": 43}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Special attention is given to the part of the parser that checks the fulfillment of logical well-formedness conditions on trees .", "output": {"relations": {"feature of": [{"head": {"text": "logical well-formedness conditions", "start": 84, "end": 118}, "tail": {"text": "trees", "start": 122, "end": 127}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We present a text mining method for finding synonymous expressions based on the distributional hypothesis in a set of coherent corpora .", "output": {"relations": {"used for": [{"head": {"text": "text mining method", "start": 13, "end": 31}, "tail": {"text": "synonymous expressions", "start": 44, "end": 66}}, {"head": {"text": "distributional hypothesis", "start": 80, "end": 105}, "tail": {"text": "text mining method", "start": 13, "end": 31}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper proposes a new methodology to improve the accuracy of a term aggregation system using each author 's text as a coherent corpus .", "output": {"relations": {"evaluate for": [{"head": {"text": "accuracy", "start": 53, "end": 61}, "tail": {"text": "term aggregation system", "start": 67, "end": 90}}, {"head": {"text": "term aggregation system", "start": 67, "end": 90}, "tail": {"text": "methodology", "start": 26, "end": 37}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Our proposed method improves the accuracy of our term aggregation system , showing that our approach is successful .", "output": {"relations": {"evaluate for": [{"head": {"text": "accuracy", "start": 33, "end": 41}, "tail": {"text": "term aggregation system", "start": 49, "end": 72}}, {"head": {"text": "term aggregation system", "start": 49, "end": 72}, "tail": {"text": "method", "start": 13, "end": 19}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this work , we present a technique for robust estimation , which by explicitly incorporating the inherent uncertainty of the estimation procedure , results in a more efficient robust estimation algorithm .", "output": {"relations": {"used for": [{"head": {"text": "technique", "start": 28, "end": 37}, "tail": {"text": "robust estimation", "start": 42, "end": 59}}, {"head": {"text": "technique", "start": 28, "end": 37}, "tail": {"text": "efficient robust estimation algorithm", "start": 169, "end": 206}}, {"head": {"text": "inherent uncertainty of the estimation procedure", "start": 100, "end": 148}, "tail": {"text": "technique", "start": 28, "end": 37}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The combination of these two strategies results in a robust estimation procedure that provides a significant speed-up over existing RANSAC techniques , while requiring no prior information to guide the sampling process .", "output": {"relations": {"used for": [{"head": {"text": "strategies", "start": 29, "end": 39}, "tail": {"text": "robust estimation procedure", "start": 53, "end": 80}}], "compare": [{"head": {"text": "RANSAC techniques", "start": 132, "end": 149}, "tail": {"text": "robust estimation procedure", "start": 53, "end": 80}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In particular , our algorithm requires , on average , 3-10 times fewer samples than standard RANSAC , which is in close agreement with theoretical predictions .", "output": {"relations": {"compare": [{"head": {"text": "algorithm", "start": 20, "end": 29}, "tail": {"text": "RANSAC", "start": 93, "end": 99}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The efficiency of the algorithm is demonstrated on a selection of geometric estimation problems .", "output": {"relations": {"evaluate for": [{"head": {"text": "geometric estimation problems", "start": 66, "end": 95}, "tail": {"text": "algorithm", "start": 22, "end": 31}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "An attempt has been made to use an Augmented Transition Network as a procedural dialog model .", "output": {"relations": {"hyponym of": [{"head": {"text": "Augmented Transition Network", "start": 35, "end": 63}, "tail": {"text": "dialog model", "start": 80, "end": 92}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The development of such a model appears to be important in several respects : as a device to represent and to use different dialog schemata proposed in empirical conversation analysis ; as a device to represent and to use models of verbal interaction ; as a device combining knowledge about dialog schemata and about verbal interaction with knowledge about task-oriented and goal-directed dialogs .", "output": {"relations": {"used for": [{"head": {"text": "dialog schemata", "start": 124, "end": 139}, "tail": {"text": "device", "start": 83, "end": 89}}, {"head": {"text": "dialog schemata", "start": 124, "end": 139}, "tail": {"text": "conversation analysis", "start": 162, "end": 183}}, {"head": {"text": "models", "start": 222, "end": 228}, "tail": {"text": "device", "start": 83, "end": 89}}, {"head": {"text": "models", "start": 222, "end": 228}, "tail": {"text": "verbal interaction", "start": 232, "end": 250}}], "conjunction": [{"head": {"text": "dialog schemata", "start": 124, "end": 139}, "tail": {"text": "verbal interaction", "start": 232, "end": 250}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "A standard ATN should be further developed in order to account for the verbal interactions of task-oriented dialogs .", "output": {"relations": {"used for": [{"head": {"text": "ATN", "start": 11, "end": 14}, "tail": {"text": "verbal interactions", "start": 71, "end": 90}}], "feature of": [{"head": {"text": "verbal interactions", "start": 71, "end": 90}, "tail": {"text": "task-oriented dialogs", "start": 94, "end": 115}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We present a practically unsupervised learning method to produce single-snippet answers to definition questions in question answering systems that supplement Web search engines .", "output": {"relations": {"used for": [{"head": {"text": "unsupervised learning method", "start": 25, "end": 53}, "tail": {"text": "single-snippet answers", "start": 65, "end": 87}}, {"head": {"text": "question answering systems", "start": 115, "end": 141}, "tail": {"text": "Web search engines", "start": 158, "end": 176}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The method exploits on-line encyclopedias and dictionaries to generate automatically an arbitrarily large number of positive and negative definition examples , which are then used to train an svm to separate the two classes .", "output": {"relations": {"used for": [{"head": {"text": "method", "start": 4, "end": 10}, "tail": {"text": "on-line encyclopedias and dictionaries", "start": 20, "end": 58}}, {"head": {"text": "on-line encyclopedias and dictionaries", "start": 20, "end": 58}, "tail": {"text": "positive and negative definition examples", "start": 116, "end": 157}}, {"head": {"text": "positive and negative definition examples", "start": 116, "end": 157}, "tail": {"text": "svm", "start": 192, "end": 195}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We show experimentally that the proposed method is viable , that it outperforms the alternative of training the system on questions and news articles from trec , and that it helps the search engine handle definition questions significantly better .", "output": {"relations": {"compare": [{"head": {"text": "it", "start": 65, "end": 67}, "tail": {"text": "alternative", "start": 84, "end": 95}}], "used for": [{"head": {"text": "news articles", "start": 136, "end": 149}, "tail": {"text": "system", "start": 112, "end": 118}}, {"head": {"text": "it", "start": 65, "end": 67}, "tail": {"text": "search engine", "start": 184, "end": 197}}], "part of": [{"head": {"text": "news articles", "start": 136, "end": 149}, "tail": {"text": "trec", "start": 155, "end": 159}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We revisit the classical decision-theoretic problem of weighted expert voting from a statistical learning perspective .", "output": {"relations": {"used for": [{"head": {"text": "statistical learning perspective", "start": 85, "end": 117}, "tail": {"text": "classical decision-theoretic problem of weighted expert voting", "start": 15, "end": 77}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In the case of known expert competence levels , we give sharp error estimates for the optimal rule .", "output": {"relations": {"used for": [{"head": {"text": "sharp error estimates", "start": 56, "end": 77}, "tail": {"text": "optimal rule", "start": 86, "end": 98}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We analyze a reweighted version of the Kikuchi approximation for estimating the log partition function of a product distribution defined over a region graph .", "output": {"relations": {"used for": [{"head": {"text": "reweighted version of the Kikuchi approximation", "start": 13, "end": 60}, "tail": {"text": "log partition function of a product distribution", "start": 80, "end": 128}}], "feature of": [{"head": {"text": "log partition function of a product distribution", "start": 80, "end": 128}, "tail": {"text": "region graph", "start": 144, "end": 156}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We establish sufficient conditions for the concavity of our reweighted objective function in terms of weight assignments in the Kikuchi expansion , and show that a reweighted version of the sum product algorithm applied to the Kikuchi region graph will produce global optima of the Kikuchi approximation whenever the algorithm converges .", "output": {"relations": {"feature of": [{"head": {"text": "concavity", "start": 43, "end": 52}, "tail": {"text": "reweighted objective function", "start": 60, "end": 89}}, {"head": {"text": "global optima", "start": 261, "end": 274}, "tail": {"text": "Kikuchi approximation", "start": 282, "end": 303}}], "used for": [{"head": {"text": "reweighted version of the sum product algorithm", "start": 164, "end": 211}, "tail": {"text": "Kikuchi region graph", "start": 227, "end": 247}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Finally , we provide an explicit characterization of the polytope of concavity in terms of the cycle structure of the region graph .", "output": {"relations": {"feature of": [{"head": {"text": "cycle structure", "start": 95, "end": 110}, "tail": {"text": "region graph", "start": 118, "end": 130}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We apply a decision tree based approach to pronoun resolution in spoken dialogue .", "output": {"relations": {"used for": [{"head": {"text": "decision tree based approach", "start": 11, "end": 39}, "tail": {"text": "pronoun resolution", "start": 43, "end": 61}}, {"head": {"text": "pronoun resolution", "start": 43, "end": 61}, "tail": {"text": "spoken dialogue", "start": 65, "end": 80}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Our system deals with pronouns with NP - and non-NP-antecedents .", "output": {"relations": {"used for": [{"head": {"text": "system", "start": 4, "end": 10}, "tail": {"text": "pronouns", "start": 22, "end": 30}}, {"head": {"text": "NP - and non-NP-antecedents", "start": 36, "end": 63}, "tail": {"text": "pronouns", "start": 22, "end": 30}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We present a set of features designed for pronoun resolution in spoken dialogue and determine the most promising features .", "output": {"relations": {"used for": [{"head": {"text": "features", "start": 20, "end": 28}, "tail": {"text": "pronoun resolution", "start": 42, "end": 60}}, {"head": {"text": "pronoun resolution", "start": 42, "end": 60}, "tail": {"text": "spoken dialogue", "start": 64, "end": 79}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We evaluate the system on twenty Switchboard dialogues and show that it compares well to Byron 's -LRB- 2002 -RRB- manually tuned system .", "output": {"relations": {"evaluate for": [{"head": {"text": "Switchboard dialogues", "start": 33, "end": 54}, "tail": {"text": "system", "start": 16, "end": 22}}], "compare": [{"head": {"text": "it", "start": 35, "end": 37}, "tail": {"text": "Byron 's -LRB- 2002 -RRB- manually tuned system", "start": 89, "end": 136}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We present a new approach for building an efficient and robust classifier for the two class problem , that localizes objects that may appear in the image under different orien-tations .", "output": {"relations": {"used for": [{"head": {"text": "approach", "start": 17, "end": 25}, "tail": {"text": "classifier", "start": 63, "end": 73}}, {"head": {"text": "classifier", "start": 63, "end": 73}, "tail": {"text": "class problem", "start": 86, "end": 99}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In contrast to other works that address this problem using multiple classifiers , each one specialized for a specific orientation , we propose a simple two-step approach with an estimation stage and a classification stage .", "output": {"relations": {"part of": [{"head": {"text": "estimation stage", "start": 178, "end": 194}, "tail": {"text": "approach", "start": 161, "end": 169}}, {"head": {"text": "classification stage", "start": 201, "end": 221}, "tail": {"text": "approach", "start": 161, "end": 169}}], "conjunction": [{"head": {"text": "estimation stage", "start": 178, "end": 194}, "tail": {"text": "classification stage", "start": 201, "end": 221}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The estimator yields an initial set of potential object poses that are then validated by the classifier .", "output": {"relations": {"used for": [{"head": {"text": "classifier", "start": 93, "end": 103}, "tail": {"text": "object poses", "start": 49, "end": 61}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This methodology allows reducing the time complexity of the algorithm while classification results remain high .", "output": {"relations": {"evaluate for": [{"head": {"text": "time complexity", "start": 37, "end": 52}, "tail": {"text": "algorithm", "start": 60, "end": 69}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The classifier we use in both stages is based on a boosted combination of Random Ferns over local histograms of oriented gradients -LRB- HOGs -RRB- , which we compute during a pre-processing step .", "output": {"relations": {"used for": [{"head": {"text": "boosted combination of Random Ferns", "start": 51, "end": 86}, "tail": {"text": "classifier", "start": 4, "end": 14}}, {"head": {"text": "pre-processing step", "start": 176, "end": 195}, "tail": {"text": "local histograms of oriented gradients -LRB- HOGs -RRB-", "start": 92, "end": 147}}], "feature of": [{"head": {"text": "local histograms of oriented gradients -LRB- HOGs -RRB-", "start": 92, "end": 147}, "tail": {"text": "boosted combination of Random Ferns", "start": 51, "end": 86}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Both the use of supervised learning and working on the gradient space makes our approach robust while being efficient at run-time .", "output": {"relations": {"used for": [{"head": {"text": "supervised learning", "start": 16, "end": 35}, "tail": {"text": "approach", "start": 80, "end": 88}}, {"head": {"text": "gradient space", "start": 55, "end": 69}, "tail": {"text": "approach", "start": 80, "end": 88}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We show these properties by thorough testing on standard databases and on a new database made of motorbikes under planar rotations , and with challenging conditions such as cluttered backgrounds , changing illumination conditions and partial occlusions .", "output": {"relations": {"feature of": [{"head": {"text": "motorbikes under planar rotations", "start": 97, "end": 130}, "tail": {"text": "database", "start": 57, "end": 65}}, {"head": {"text": "conditions", "start": 154, "end": 164}, "tail": {"text": "database", "start": 57, "end": 65}}], "hyponym of": [{"head": {"text": "cluttered backgrounds", "start": 173, "end": 194}, "tail": {"text": "conditions", "start": 154, "end": 164}}, {"head": {"text": "changing illumination conditions", "start": 197, "end": 229}, "tail": {"text": "conditions", "start": 154, "end": 164}}, {"head": {"text": "partial occlusions", "start": 234, "end": 252}, "tail": {"text": "conditions", "start": 154, "end": 164}}], "conjunction": [{"head": {"text": "cluttered backgrounds", "start": 173, "end": 194}, "tail": {"text": "changing illumination conditions", "start": 197, "end": 229}}, {"head": {"text": "changing illumination conditions", "start": 197, "end": 229}, "tail": {"text": "partial occlusions", "start": 234, "end": 252}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "A very simple improved duration model has reduced the error rate by about 10 % in both triphone and semiphone systems .", "output": {"relations": {"used for": [{"head": {"text": "duration model", "start": 23, "end": 37}, "tail": {"text": "triphone and semiphone systems", "start": 87, "end": 117}}], "evaluate for": [{"head": {"text": "error rate", "start": 54, "end": 64}, "tail": {"text": "triphone and semiphone systems", "start": 87, "end": 117}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "A new training strategy has been tested which , by itself , did not provide useful improvements but suggests that improvements can be obtained by a related rapid adaptation technique .", "output": {"relations": {"used for": [{"head": {"text": "rapid adaptation technique", "start": 156, "end": 182}, "tail": {"text": "training strategy", "start": 6, "end": 23}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Finally , the recognizer has been modified to use bigram back-off language models .", "output": {"relations": {"used for": [{"head": {"text": "bigram back-off language models", "start": 50, "end": 81}, "tail": {"text": "recognizer", "start": 14, "end": 24}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The system was then transferred from the RM task to the ATIS CSR task and a limited number of development tests performed .", "output": {"relations": {"used for": [{"head": {"text": "system", "start": 4, "end": 10}, "tail": {"text": "RM task", "start": 41, "end": 48}}, {"head": {"text": "system", "start": 4, "end": 10}, "tail": {"text": "ATIS CSR task", "start": 56, "end": 69}}], "conjunction": [{"head": {"text": "RM task", "start": 41, "end": 48}, "tail": {"text": "ATIS CSR task", "start": 56, "end": 69}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "A new approach for Interactive Machine Translation where the author interacts during the creation or the modification of the document is proposed .", "output": {"relations": {"used for": [{"head": {"text": "approach", "start": 6, "end": 14}, "tail": {"text": "Interactive Machine Translation", "start": 19, "end": 50}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper presents a new interactive disambiguation scheme based on the paraphrasing of a parser 's multiple output .", "output": {"relations": {"used for": [{"head": {"text": "paraphrasing", "start": 73, "end": 85}, "tail": {"text": "interactive disambiguation scheme", "start": 26, "end": 59}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We describe a novel approach to statistical machine translation that combines syntactic information in the source language with recent advances in phrasal translation .", "output": {"relations": {"used for": [{"head": {"text": "approach", "start": 20, "end": 28}, "tail": {"text": "statistical machine translation", "start": 32, "end": 63}}], "part of": [{"head": {"text": "syntactic information", "start": 78, "end": 99}, "tail": {"text": "approach", "start": 20, "end": 28}}, {"head": {"text": "phrasal translation", "start": 147, "end": 166}, "tail": {"text": "approach", "start": 20, "end": 28}}], "conjunction": [{"head": {"text": "syntactic information", "start": 78, "end": 99}, "tail": {"text": "phrasal translation", "start": 147, "end": 166}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This method requires a source-language dependency parser , target language word segmentation and an unsupervised word alignment component .", "output": {"relations": {"used for": [{"head": {"text": "source-language dependency parser", "start": 23, "end": 56}, "tail": {"text": "method", "start": 5, "end": 11}}, {"head": {"text": "target language word segmentation", "start": 59, "end": 92}, "tail": {"text": "method", "start": 5, "end": 11}}, {"head": {"text": "unsupervised word alignment component", "start": 100, "end": 137}, "tail": {"text": "method", "start": 5, "end": 11}}], "conjunction": [{"head": {"text": "source-language dependency parser", "start": 23, "end": 56}, "tail": {"text": "target language word segmentation", "start": 59, "end": 92}}, {"head": {"text": "target language word segmentation", "start": 59, "end": 92}, "tail": {"text": "unsupervised word alignment component", "start": 100, "end": 137}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We describe an efficient decoder and show that using these tree-based models in combination with conventional SMT models provides a promising approach that incorporates the power of phrasal SMT with the linguistic generality available in a parser .", "output": {"relations": {"conjunction": [{"head": {"text": "tree-based models", "start": 59, "end": 76}, "tail": {"text": "SMT models", "start": 110, "end": 120}}, {"head": {"text": "phrasal SMT", "start": 182, "end": 193}, "tail": {"text": "linguistic generality", "start": 203, "end": 224}}], "used for": [{"head": {"text": "tree-based models", "start": 59, "end": 76}, "tail": {"text": "approach", "start": 142, "end": 150}}, {"head": {"text": "SMT models", "start": 110, "end": 120}, "tail": {"text": "approach", "start": 142, "end": 150}}, {"head": {"text": "phrasal SMT", "start": 182, "end": 193}, "tail": {"text": "parser", "start": 240, "end": 246}}], "feature of": [{"head": {"text": "linguistic generality", "start": 203, "end": 224}, "tail": {"text": "parser", "start": 240, "end": 246}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Video provides not only rich visual cues such as motion and appearance , but also much less explored long-range temporal interactions among objects .", "output": {"relations": {"feature of": [{"head": {"text": "visual cues", "start": 29, "end": 40}, "tail": {"text": "Video", "start": 0, "end": 5}}], "hyponym of": [{"head": {"text": "motion", "start": 49, "end": 55}, "tail": {"text": "visual cues", "start": 29, "end": 40}}, {"head": {"text": "appearance", "start": 60, "end": 70}, "tail": {"text": "visual cues", "start": 29, "end": 40}}], "conjunction": [{"head": {"text": "motion", "start": 49, "end": 55}, "tail": {"text": "appearance", "start": 60, "end": 70}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We aim to capture such interactions and to construct a powerful intermediate-level video representation for subsequent recognition .", "output": {"relations": {"used for": [{"head": {"text": "intermediate-level video representation", "start": 64, "end": 103}, "tail": {"text": "recognition", "start": 119, "end": 130}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "First , we develop an efficient spatio-temporal video segmentation algorithm , which naturally incorporates long-range motion cues from the past and future frames in the form of clusters of point tracks with coherent motion .", "output": {"relations": {"used for": [{"head": {"text": "long-range motion cues", "start": 108, "end": 130}, "tail": {"text": "spatio-temporal video segmentation algorithm", "start": 32, "end": 76}}, {"head": {"text": "clusters of point tracks", "start": 178, "end": 202}, "tail": {"text": "long-range motion cues", "start": 108, "end": 130}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Second , we devise a new track clustering cost function that includes occlusion reasoning , in the form of depth ordering constraints , as well as motion similarity along the tracks .", "output": {"relations": {"part of": [{"head": {"text": "occlusion reasoning", "start": 70, "end": 89}, "tail": {"text": "track clustering cost function", "start": 25, "end": 55}}, {"head": {"text": "motion similarity", "start": 147, "end": 164}, "tail": {"text": "track clustering cost function", "start": 25, "end": 55}}], "feature of": [{"head": {"text": "depth ordering constraints", "start": 107, "end": 133}, "tail": {"text": "occlusion reasoning", "start": 70, "end": 89}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We evaluate the proposed approach on a challenging set of video sequences of office scenes from feature length movies .", "output": {"relations": {"evaluate for": [{"head": {"text": "video sequences of office scenes", "start": 58, "end": 90}, "tail": {"text": "approach", "start": 25, "end": 33}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper , we introduce KAZE features , a novel multiscale 2D feature detection and description algorithm in nonlinear scale spaces .", "output": {"relations": {"hyponym of": [{"head": {"text": "KAZE features", "start": 29, "end": 42}, "tail": {"text": "multiscale 2D feature detection and description algorithm", "start": 53, "end": 110}}], "feature of": [{"head": {"text": "nonlinear scale spaces", "start": 114, "end": 136}, "tail": {"text": "multiscale 2D feature detection and description algorithm", "start": 53, "end": 110}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In contrast , we detect and describe 2D features in a nonlinear scale space by means of nonlinear diffusion filtering .", "output": {"relations": {"feature of": [{"head": {"text": "nonlinear scale space", "start": 54, "end": 75}, "tail": {"text": "2D features", "start": 37, "end": 48}}], "used for": [{"head": {"text": "nonlinear diffusion filtering", "start": 88, "end": 117}, "tail": {"text": "2D features", "start": 37, "end": 48}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The nonlinear scale space is built using efficient Additive Operator Splitting -LRB- AOS -RRB- techniques and variable con-ductance diffusion .", "output": {"relations": {"used for": [{"head": {"text": "Additive Operator Splitting -LRB- AOS -RRB- techniques", "start": 51, "end": 105}, "tail": {"text": "nonlinear scale space", "start": 4, "end": 25}}, {"head": {"text": "variable con-ductance diffusion", "start": 110, "end": 141}, "tail": {"text": "nonlinear scale space", "start": 4, "end": 25}}], "conjunction": [{"head": {"text": "Additive Operator Splitting -LRB- AOS -RRB- techniques", "start": 51, "end": 105}, "tail": {"text": "variable con-ductance diffusion", "start": 110, "end": 141}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Even though our features are somewhat more expensive to compute than SURF due to the construction of the nonlinear scale space , but comparable to SIFT , our results reveal a step forward in performance both in detection and description against previous state-of-the-art methods .", "output": {"relations": {"compare": [{"head": {"text": "features", "start": 16, "end": 24}, "tail": {"text": "SURF", "start": 69, "end": 73}}, {"head": {"text": "features", "start": 16, "end": 24}, "tail": {"text": "SIFT", "start": 147, "end": 151}}, {"head": {"text": "results", "start": 158, "end": 165}, "tail": {"text": "state-of-the-art methods", "start": 254, "end": 278}}], "evaluate for": [{"head": {"text": "detection", "start": 211, "end": 220}, "tail": {"text": "results", "start": 158, "end": 165}}, {"head": {"text": "detection", "start": 211, "end": 220}, "tail": {"text": "state-of-the-art methods", "start": 254, "end": 278}}, {"head": {"text": "description", "start": 225, "end": 236}, "tail": {"text": "results", "start": 158, "end": 165}}, {"head": {"text": "description", "start": 225, "end": 236}, "tail": {"text": "state-of-the-art methods", "start": 254, "end": 278}}], "conjunction": [{"head": {"text": "detection", "start": 211, "end": 220}, "tail": {"text": "description", "start": 225, "end": 236}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Creating summaries on lengthy Semantic Web documents for quick identification of the corresponding entity has been of great contemporary interest .", "output": {"relations": {"used for": [{"head": {"text": "Creating summaries", "start": 0, "end": 18}, "tail": {"text": "identification of the corresponding entity", "start": 63, "end": 105}}, {"head": {"text": "lengthy Semantic Web documents", "start": 22, "end": 52}, "tail": {"text": "Creating summaries", "start": 0, "end": 18}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Specifically , we highlight the importance of diversified -LRB- faceted -RRB- summaries by combining three dimensions : diversity , uniqueness , and popularity .", "output": {"relations": {"feature of": [{"head": {"text": "diversity", "start": 120, "end": 129}, "tail": {"text": "diversified -LRB- faceted -RRB- summaries", "start": 46, "end": 87}}, {"head": {"text": "uniqueness", "start": 132, "end": 142}, "tail": {"text": "diversified -LRB- faceted -RRB- summaries", "start": 46, "end": 87}}, {"head": {"text": "popularity", "start": 149, "end": 159}, "tail": {"text": "diversified -LRB- faceted -RRB- summaries", "start": 46, "end": 87}}], "conjunction": [{"head": {"text": "diversity", "start": 120, "end": 129}, "tail": {"text": "uniqueness", "start": 132, "end": 142}}, {"head": {"text": "uniqueness", "start": 132, "end": 142}, "tail": {"text": "popularity", "start": 149, "end": 159}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Our novel diversity-aware entity summarization approach mimics human conceptual clustering techniques to group facts , and picks representative facts from each group to form concise -LRB- i.e. , short -RRB- and comprehensive -LRB- i.e. , improved coverage through diversity -RRB- summaries .", "output": {"relations": {"used for": [{"head": {"text": "human conceptual clustering techniques", "start": 63, "end": 101}, "tail": {"text": "diversity-aware entity summarization approach", "start": 10, "end": 55}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We evaluate our approach against the state-of-the-art techniques and show that our work improves both the quality and the efficiency of entity summarization .", "output": {"relations": {"used for": [{"head": {"text": "approach", "start": 16, "end": 24}, "tail": {"text": "entity summarization", "start": 136, "end": 156}}, {"head": {"text": "state-of-the-art techniques", "start": 37, "end": 64}, "tail": {"text": "entity summarization", "start": 136, "end": 156}}], "compare": [{"head": {"text": "state-of-the-art techniques", "start": 37, "end": 64}, "tail": {"text": "approach", "start": 16, "end": 24}}], "evaluate for": [{"head": {"text": "quality", "start": 106, "end": 113}, "tail": {"text": "entity summarization", "start": 136, "end": 156}}, {"head": {"text": "efficiency", "start": 122, "end": 132}, "tail": {"text": "entity summarization", "start": 136, "end": 156}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We present a framework for the fast computation of lexical affinity models .", "output": {"relations": {"used for": [{"head": {"text": "framework", "start": 13, "end": 22}, "tail": {"text": "fast computation of lexical affinity models", "start": 31, "end": 74}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The framework is composed of a novel algorithm to efficiently compute the co-occurrence distribution between pairs of terms , an independence model , and a parametric affinity model .", "output": {"relations": {"part of": [{"head": {"text": "algorithm", "start": 37, "end": 46}, "tail": {"text": "framework", "start": 4, "end": 13}}, {"head": {"text": "independence model", "start": 129, "end": 147}, "tail": {"text": "framework", "start": 4, "end": 13}}, {"head": {"text": "parametric affinity model", "start": 156, "end": 181}, "tail": {"text": "framework", "start": 4, "end": 13}}], "used for": [{"head": {"text": "algorithm", "start": 37, "end": 46}, "tail": {"text": "co-occurrence distribution", "start": 74, "end": 100}}], "conjunction": [{"head": {"text": "algorithm", "start": 37, "end": 46}, "tail": {"text": "independence model", "start": 129, "end": 147}}, {"head": {"text": "independence model", "start": 129, "end": 147}, "tail": {"text": "parametric affinity model", "start": 156, "end": 181}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In comparison with previous models , which either use arbitrary windows to compute similarity between words or use lexical affinity to create sequential models , in this paper we focus on models intended to capture the co-occurrence patterns of any pair of words or phrases at any distance in the corpus .", "output": {"relations": {"used for": [{"head": {"text": "lexical affinity", "start": 115, "end": 131}, "tail": {"text": "sequential models", "start": 142, "end": 159}}, {"head": {"text": "models", "start": 28, "end": 34}, "tail": {"text": "co-occurrence patterns", "start": 219, "end": 241}}], "compare": [{"head": {"text": "models", "start": 28, "end": 34}, "tail": {"text": "models", "start": 28, "end": 34}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We apply it in combination with a terabyte corpus to answer natural language tests , achieving encouraging results .", "output": {"relations": {"used for": [{"head": {"text": "it", "start": 9, "end": 11}, "tail": {"text": "natural language tests", "start": 60, "end": 82}}], "evaluate for": [{"head": {"text": "terabyte corpus", "start": 34, "end": 49}, "tail": {"text": "it", "start": 9, "end": 11}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper introduces a system for categorizing unknown words .", "output": {"relations": {"used for": [{"head": {"text": "system", "start": 24, "end": 30}, "tail": {"text": "categorizing unknown words", "start": 35, "end": 61}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The system is based on a multi-component architecture where each component is responsible for identifying one class of unknown words .", "output": {"relations": {"used for": [{"head": {"text": "multi-component architecture", "start": 25, "end": 53}, "tail": {"text": "system", "start": 4, "end": 10}}, {"head": {"text": "component", "start": 31, "end": 40}, "tail": {"text": "unknown words", "start": 119, "end": 132}}], "part of": [{"head": {"text": "component", "start": 31, "end": 40}, "tail": {"text": "multi-component architecture", "start": 25, "end": 53}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The focus of this paper is the components that identify names and spelling errors .", "output": {"relations": {"used for": [{"head": {"text": "components", "start": 31, "end": 41}, "tail": {"text": "names", "start": 56, "end": 61}}, {"head": {"text": "components", "start": 31, "end": 41}, "tail": {"text": "spelling errors", "start": 66, "end": 81}}], "conjunction": [{"head": {"text": "names", "start": 56, "end": 61}, "tail": {"text": "spelling errors", "start": 66, "end": 81}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Each component uses a decision tree architecture to combine multiple types of evidence about the unknown word .", "output": {"relations": {"used for": [{"head": {"text": "decision tree architecture", "start": 22, "end": 48}, "tail": {"text": "component", "start": 5, "end": 14}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The system is evaluated using data from live closed captions - a genre replete with a wide variety of unknown words .", "output": {"relations": {"evaluate for": [{"head": {"text": "live closed captions", "start": 40, "end": 60}, "tail": {"text": "system", "start": 4, "end": 10}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "At MIT Lincoln Laboratory , we have been developing a Korean-to-English machine translation system CCLINC -LRB- Common Coalition Language System at Lincoln Laboratory -RRB- .", "output": {"relations": {"hyponym of": [{"head": {"text": "CCLINC -LRB- Common Coalition Language System at Lincoln Laboratory -RRB-", "start": 99, "end": 172}, "tail": {"text": "Korean-to-English machine translation system", "start": 54, "end": 98}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The CCLINC Korean-to-English translation system consists of two core modules , language understanding and generation modules mediated by a language neutral meaning representation called a semantic frame .", "output": {"relations": {"part of": [{"head": {"text": "core modules", "start": 64, "end": 76}, "tail": {"text": "CCLINC Korean-to-English translation system", "start": 4, "end": 47}}], "used for": [{"head": {"text": "language neutral meaning representation", "start": 139, "end": 178}, "tail": {"text": "language understanding and generation modules", "start": 79, "end": 124}}], "hyponym of": [{"head": {"text": "semantic frame", "start": 188, "end": 202}, "tail": {"text": "language neutral meaning representation", "start": 139, "end": 178}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The key features of the system include : -LRB- i -RRB- Robust efficient parsing of Korean -LRB- a verb final language with overt case markers , relatively free word order , and frequent omissions of arguments -RRB- .", "output": {"relations": {"hyponym of": [{"head": {"text": "Korean", "start": 83, "end": 89}, "tail": {"text": "verb final language", "start": 98, "end": 117}}], "feature of": [{"head": {"text": "overt case markers", "start": 123, "end": 141}, "tail": {"text": "verb final language", "start": 98, "end": 117}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "-LRB- ii -RRB- High quality translation via word sense disambiguation and accurate word order generation of the target language .", "output": {"relations": {"used for": [{"head": {"text": "word sense disambiguation", "start": 44, "end": 69}, "tail": {"text": "translation", "start": 28, "end": 39}}, {"head": {"text": "word order generation", "start": 83, "end": 104}, "tail": {"text": "translation", "start": 28, "end": 39}}], "conjunction": [{"head": {"text": "word sense disambiguation", "start": 44, "end": 69}, "tail": {"text": "word order generation", "start": 83, "end": 104}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Having been trained on Korean newspaper articles on missiles and chemical biological warfare , the system produces the translation output sufficient for content understanding of the original document .", "output": {"relations": {"used for": [{"head": {"text": "Korean newspaper articles", "start": 23, "end": 48}, "tail": {"text": "system", "start": 99, "end": 105}}], "feature of": [{"head": {"text": "missiles and chemical biological warfare", "start": 52, "end": 92}, "tail": {"text": "Korean newspaper articles", "start": 23, "end": 48}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The JAVELIN system integrates a flexible , planning-based architecture with a variety of language processing modules to provide an open-domain question answering capability on free text .", "output": {"relations": {"used for": [{"head": {"text": "JAVELIN system", "start": 4, "end": 18}, "tail": {"text": "open-domain question answering capability", "start": 131, "end": 172}}], "part of": [{"head": {"text": "planning-based architecture", "start": 43, "end": 70}, "tail": {"text": "JAVELIN system", "start": 4, "end": 18}}, {"head": {"text": "language processing modules", "start": 89, "end": 116}, "tail": {"text": "JAVELIN system", "start": 4, "end": 18}}], "conjunction": [{"head": {"text": "language processing modules", "start": 89, "end": 116}, "tail": {"text": "planning-based architecture", "start": 43, "end": 70}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We present the first application of the head-driven statistical parsing model of Collins -LRB- 1999 -RRB- as a simultaneous language model and parser for large-vocabulary speech recognition .", "output": {"relations": {"used for": [{"head": {"text": "head-driven statistical parsing model", "start": 40, "end": 77}, "tail": {"text": "simultaneous language model", "start": 111, "end": 138}}, {"head": {"text": "head-driven statistical parsing model", "start": 40, "end": 77}, "tail": {"text": "parser", "start": 143, "end": 149}}, {"head": {"text": "simultaneous language model", "start": 111, "end": 138}, "tail": {"text": "large-vocabulary speech recognition", "start": 154, "end": 189}}, {"head": {"text": "parser", "start": 143, "end": 149}, "tail": {"text": "large-vocabulary speech recognition", "start": 154, "end": 189}}], "conjunction": [{"head": {"text": "simultaneous language model", "start": 111, "end": 138}, "tail": {"text": "parser", "start": 143, "end": 149}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The model is adapted to an online left to right chart-parser for word lattices , integrating acoustic , n-gram , and parser probabilities .", "output": {"relations": {"used for": [{"head": {"text": "model", "start": 4, "end": 9}, "tail": {"text": "online left to right chart-parser", "start": 27, "end": 60}}, {"head": {"text": "online left to right chart-parser", "start": 27, "end": 60}, "tail": {"text": "word lattices", "start": 65, "end": 78}}], "part of": [{"head": {"text": "acoustic , n-gram , and parser probabilities", "start": 93, "end": 137}, "tail": {"text": "online left to right chart-parser", "start": 27, "end": 60}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The parser uses structural and lexical dependencies not considered by n-gram models , conditioning recognition on more linguistically-grounded relationships .", "output": {"relations": {"used for": [{"head": {"text": "structural and lexical dependencies", "start": 16, "end": 51}, "tail": {"text": "parser", "start": 4, "end": 10}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Experiments on the Wall Street Journal treebank and lattice corpora show word error rates competitive with the standard n-gram language model while extracting additional structural information useful for speech understanding .", "output": {"relations": {"conjunction": [{"head": {"text": "Wall Street Journal treebank", "start": 19, "end": 47}, "tail": {"text": "lattice corpora", "start": 52, "end": 67}}], "evaluate for": [{"head": {"text": "Wall Street Journal treebank", "start": 19, "end": 47}, "tail": {"text": "n-gram language model", "start": 120, "end": 141}}, {"head": {"text": "lattice corpora", "start": 52, "end": 67}, "tail": {"text": "n-gram language model", "start": 120, "end": 141}}, {"head": {"text": "word error rates", "start": 73, "end": 89}, "tail": {"text": "n-gram language model", "start": 120, "end": 141}}], "used for": [{"head": {"text": "structural information", "start": 170, "end": 192}, "tail": {"text": "speech understanding", "start": 204, "end": 224}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Image composition -LRB- or mosaicing -RRB- has attracted a growing attention in recent years as one of the main elements in video analysis and representation .", "output": {"relations": {"part of": [{"head": {"text": "Image composition -LRB- or mosaicing -RRB-", "start": 0, "end": 42}, "tail": {"text": "video analysis and representation", "start": 124, "end": 157}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper we deal with the problem of global alignment and super-resolution .", "output": {"relations": {"conjunction": [{"head": {"text": "global alignment", "start": 42, "end": 58}, "tail": {"text": "super-resolution", "start": 63, "end": 79}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We also propose to evaluate the quality of the resulting mosaic by measuring the amount of blurring .", "output": {"relations": {"evaluate for": [{"head": {"text": "amount of blurring", "start": 81, "end": 99}, "tail": {"text": "mosaic", "start": 57, "end": 63}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Global registration is achieved by combining a graph-based technique -- that exploits the topological structure of the sequence induced by the spatial overlap -- with a bundle adjustment which uses only the homographies computed in the previous steps .", "output": {"relations": {"used for": [{"head": {"text": "graph-based technique", "start": 47, "end": 68}, "tail": {"text": "Global registration", "start": 0, "end": 19}}, {"head": {"text": "graph-based technique", "start": 47, "end": 68}, "tail": {"text": "topological structure", "start": 90, "end": 111}}, {"head": {"text": "bundle adjustment", "start": 169, "end": 186}, "tail": {"text": "Global registration", "start": 0, "end": 19}}, {"head": {"text": "homographies", "start": 207, "end": 219}, "tail": {"text": "bundle adjustment", "start": 169, "end": 186}}], "conjunction": [{"head": {"text": "graph-based technique", "start": 47, "end": 68}, "tail": {"text": "bundle adjustment", "start": 169, "end": 186}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Experimental comparison with other techniques shows the effectiveness of our approach .", "output": {"relations": {"compare": [{"head": {"text": "approach", "start": 77, "end": 85}, "tail": {"text": "techniques", "start": 35, "end": 45}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The main of this project is computer-assisted acquisition and morpho-syntactic description of verb-noun collocations in Polish .", "output": {"relations": {"used for": [{"head": {"text": "Polish", "start": 120, "end": 126}, "tail": {"text": "computer-assisted acquisition and morpho-syntactic description of verb-noun collocations", "start": 28, "end": 116}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We present methodology and resources obtained in three main project phases which are : dictionary-based acquisition of collocation lexicon , feasibility study for corpus-based lexicon enlargement phase , corpus-based lexicon enlargement and collocation description .", "output": {"relations": {"hyponym of": [{"head": {"text": "dictionary-based acquisition of collocation lexicon", "start": 87, "end": 138}, "tail": {"text": "phases", "start": 68, "end": 74}}, {"head": {"text": "feasibility study", "start": 141, "end": 158}, "tail": {"text": "phases", "start": 68, "end": 74}}, {"head": {"text": "corpus-based lexicon enlargement and collocation description", "start": 204, "end": 264}, "tail": {"text": "phases", "start": 68, "end": 74}}], "conjunction": [{"head": {"text": "dictionary-based acquisition of collocation lexicon", "start": 87, "end": 138}, "tail": {"text": "feasibility study", "start": 141, "end": 158}}, {"head": {"text": "corpus-based lexicon enlargement and collocation description", "start": 204, "end": 264}, "tail": {"text": "feasibility study", "start": 141, "end": 158}}], "used for": [{"head": {"text": "feasibility study", "start": 141, "end": 158}, "tail": {"text": "corpus-based lexicon enlargement phase", "start": 163, "end": 201}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The presented here corpus-based approach permitted us to triple the size the verb-noun collocation dictionary for Polish .", "output": {"relations": {"used for": [{"head": {"text": "corpus-based approach", "start": 19, "end": 40}, "tail": {"text": "verb-noun collocation dictionary", "start": 77, "end": 109}}], "feature of": [{"head": {"text": "Polish", "start": 114, "end": 120}, "tail": {"text": "verb-noun collocation dictionary", "start": 77, "end": 109}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Along with the increasing requirements , the hash-tag recommendation task for microblogs has been receiving considerable attention in recent years .", "output": {"relations": {"used for": [{"head": {"text": "hash-tag recommendation task", "start": 45, "end": 73}, "tail": {"text": "microblogs", "start": 78, "end": 88}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Motivated by the successful use of convolutional neural networks -LRB- CNNs -RRB- for many natural language processing tasks , in this paper , we adopt CNNs to perform the hashtag recommendation problem .", "output": {"relations": {"used for": [{"head": {"text": "convolutional neural networks -LRB- CNNs -RRB-", "start": 35, "end": 81}, "tail": {"text": "natural language processing tasks", "start": 91, "end": 124}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "To incorporate the trigger words whose effectiveness have been experimentally evaluated in several previous works , we propose a novel architecture with an attention mechanism .", "output": {"relations": {"used for": [{"head": {"text": "architecture", "start": 135, "end": 147}, "tail": {"text": "trigger words", "start": 19, "end": 32}}], "feature of": [{"head": {"text": "attention mechanism", "start": 156, "end": 175}, "tail": {"text": "architecture", "start": 135, "end": 147}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The results of experiments on the data collected from a real world microblogging service demonstrated that the proposed model outperforms state-of-the-art methods .", "output": {"relations": {"evaluate for": [{"head": {"text": "data", "start": 34, "end": 38}, "tail": {"text": "model", "start": 120, "end": 125}}], "compare": [{"head": {"text": "model", "start": 120, "end": 125}, "tail": {"text": "state-of-the-art methods", "start": 138, "end": 162}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "By incorporating trigger words into the consideration , the relative improvement of the proposed method over the state-of-the-art method is around 9.4 % in the F1-score .", "output": {"relations": {"compare": [{"head": {"text": "method", "start": 97, "end": 103}, "tail": {"text": "state-of-the-art method", "start": 113, "end": 136}}], "evaluate for": [{"head": {"text": "F1-score", "start": 160, "end": 168}, "tail": {"text": "state-of-the-art method", "start": 113, "end": 136}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper , we improve an unsupervised learning method using the Expectation-Maximization -LRB- EM -RRB- algorithm proposed by Nigam et al. for text classification problems in order to apply it to word sense disambiguation -LRB- WSD -RRB- problems .", "output": {"relations": {"used for": [{"head": {"text": "Expectation-Maximization -LRB- EM -RRB- algorithm", "start": 69, "end": 118}, "tail": {"text": "unsupervised learning method", "start": 30, "end": 58}}, {"head": {"text": "Expectation-Maximization -LRB- EM -RRB- algorithm", "start": 69, "end": 118}, "tail": {"text": "text classification problems", "start": 148, "end": 176}}, {"head": {"text": "it", "start": 114, "end": 116}, "tail": {"text": "word sense disambiguation -LRB- WSD -RRB- problems", "start": 201, "end": 251}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In experiments , we solved 50 noun WSD problems in the Japanese Dictionary Task in SENSEVAL2 .", "output": {"relations": {"feature of": [{"head": {"text": "Japanese Dictionary Task", "start": 55, "end": 79}, "tail": {"text": "SENSEVAL2", "start": 83, "end": 92}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Furthermore , our methods were confirmed to be effective also for verb WSD problems .", "output": {"relations": {"used for": [{"head": {"text": "methods", "start": 18, "end": 25}, "tail": {"text": "verb WSD problems", "start": 66, "end": 83}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Dividing sentences in chunks of words is a useful preprocessing step for parsing , information extraction and information retrieval .", "output": {"relations": {"used for": [{"head": {"text": "Dividing sentences in chunks of words", "start": 0, "end": 37}, "tail": {"text": "parsing", "start": 73, "end": 80}}, {"head": {"text": "Dividing sentences in chunks of words", "start": 0, "end": 37}, "tail": {"text": "information extraction", "start": 83, "end": 105}}, {"head": {"text": "Dividing sentences in chunks of words", "start": 0, "end": 37}, "tail": {"text": "information retrieval", "start": 110, "end": 131}}], "conjunction": [{"head": {"text": "parsing", "start": 73, "end": 80}, "tail": {"text": "information extraction", "start": 83, "end": 105}}, {"head": {"text": "information extraction", "start": 83, "end": 105}, "tail": {"text": "information retrieval", "start": 110, "end": 131}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "-LRB- Ramshaw and Marcus , 1995 -RRB- have introduced a `` convenient '' data representation for chunking by converting it to a tagging task .", "output": {"relations": {"used for": [{"head": {"text": "data representation", "start": 73, "end": 92}, "tail": {"text": "chunking", "start": 97, "end": 105}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper we will examine seven different data representations for the problem of recognizing noun phrase chunks .", "output": {"relations": {"used for": [{"head": {"text": "data representations", "start": 46, "end": 66}, "tail": {"text": "recognizing noun phrase chunks", "start": 86, "end": 116}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "However , equipped with the most suitable data representation , our memory-based learning chunker was able to improve the best published chunking results for a standard data set .", "output": {"relations": {"used for": [{"head": {"text": "data representation", "start": 42, "end": 61}, "tail": {"text": "memory-based learning chunker", "start": 68, "end": 97}}], "evaluate for": [{"head": {"text": "data set", "start": 169, "end": 177}, "tail": {"text": "memory-based learning chunker", "start": 68, "end": 97}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We focus on FAQ-like questions and answers , and build our system around a noisy-channel architecture which exploits both a language model for answers and a transformation model for answer/question terms , trained on a corpus of 1 million question/answer pairs collected from the Web .", "output": {"relations": {"used for": [{"head": {"text": "system", "start": 59, "end": 65}, "tail": {"text": "FAQ-like questions and answers", "start": 12, "end": 42}}, {"head": {"text": "noisy-channel architecture", "start": 75, "end": 101}, "tail": {"text": "system", "start": 59, "end": 65}}, {"head": {"text": "noisy-channel architecture", "start": 75, "end": 101}, "tail": {"text": "language model", "start": 124, "end": 138}}, {"head": {"text": "noisy-channel architecture", "start": 75, "end": 101}, "tail": {"text": "transformation model", "start": 157, "end": 177}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper we evaluate four objective measures of speech with regards to intelligibility prediction of synthesized speech in diverse noisy situations .", "output": {"relations": {"evaluate for": [{"head": {"text": "measures of speech", "start": 41, "end": 59}, "tail": {"text": "intelligibility prediction", "start": 76, "end": 102}}], "used for": [{"head": {"text": "synthesized speech", "start": 106, "end": 124}, "tail": {"text": "intelligibility prediction", "start": 76, "end": 102}}], "feature of": [{"head": {"text": "diverse noisy situations", "start": 128, "end": 152}, "tail": {"text": "synthesized speech", "start": 106, "end": 124}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We evaluated three intel-ligibility measures , the Dau measure , the glimpse proportion and the Speech Intelligibility Index -LRB- SII -RRB- and a quality measure , the Perceptual Evaluation of Speech Quality -LRB- PESQ -RRB- .", "output": {"relations": {"conjunction": [{"head": {"text": "intel-ligibility measures", "start": 19, "end": 44}, "tail": {"text": "quality measure", "start": 147, "end": 162}}, {"head": {"text": "Dau measure", "start": 51, "end": 62}, "tail": {"text": "glimpse proportion", "start": 69, "end": 87}}, {"head": {"text": "glimpse proportion", "start": 69, "end": 87}, "tail": {"text": "Speech Intelligibility Index -LRB- SII -RRB-", "start": 96, "end": 140}}], "hyponym of": [{"head": {"text": "Dau measure", "start": 51, "end": 62}, "tail": {"text": "intel-ligibility measures", "start": 19, "end": 44}}, {"head": {"text": "glimpse proportion", "start": 69, "end": 87}, "tail": {"text": "intel-ligibility measures", "start": 19, "end": 44}}, {"head": {"text": "Speech Intelligibility Index -LRB- SII -RRB-", "start": 96, "end": 140}, "tail": {"text": "intel-ligibility measures", "start": 19, "end": 44}}, {"head": {"text": "Perceptual Evaluation of Speech Quality -LRB- PESQ -RRB-", "start": 169, "end": 225}, "tail": {"text": "quality measure", "start": 147, "end": 162}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "For the generation of synthesized speech we used a state of the art HMM-based speech synthesis system .", "output": {"relations": {"used for": [{"head": {"text": "HMM-based speech synthesis system", "start": 68, "end": 101}, "tail": {"text": "generation of synthesized speech", "start": 8, "end": 40}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The noisy conditions comprised four additive noises .", "output": {"relations": {"part of": [{"head": {"text": "additive noises", "start": 36, "end": 51}, "tail": {"text": "noisy conditions", "start": 4, "end": 20}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The measures were compared with subjective intelligibility scores obtained in listening tests .", "output": {"relations": {"compare": [{"head": {"text": "measures", "start": 4, "end": 12}, "tail": {"text": "subjective intelligibility scores", "start": 32, "end": 65}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The results show the Dau and the glimpse measures to be the best predictors of intelligibility , with correlations of around 0.83 to subjective scores .", "output": {"relations": {"conjunction": [{"head": {"text": "Dau", "start": 21, "end": 24}, "tail": {"text": "glimpse measures", "start": 33, "end": 49}}], "hyponym of": [{"head": {"text": "Dau", "start": 21, "end": 24}, "tail": {"text": "predictors of intelligibility", "start": 65, "end": 94}}, {"head": {"text": "glimpse measures", "start": 33, "end": 49}, "tail": {"text": "predictors of intelligibility", "start": 65, "end": 94}}], "compare": [{"head": {"text": "Dau", "start": 21, "end": 24}, "tail": {"text": "subjective scores", "start": 133, "end": 150}}, {"head": {"text": "glimpse measures", "start": 33, "end": 49}, "tail": {"text": "subjective scores", "start": 133, "end": 150}}], "evaluate for": [{"head": {"text": "correlations", "start": 102, "end": 114}, "tail": {"text": "Dau", "start": 21, "end": 24}}, {"head": {"text": "correlations", "start": 102, "end": 114}, "tail": {"text": "glimpse measures", "start": 33, "end": 49}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "All measures gave less accurate predictions of intelligibility for synthetic speech than have previously been found for natural speech ; in particular the SII measure .", "output": {"relations": {"evaluate for": [{"head": {"text": "measures", "start": 4, "end": 12}, "tail": {"text": "predictions of intelligibility", "start": 32, "end": 62}}], "used for": [{"head": {"text": "synthetic speech", "start": 67, "end": 83}, "tail": {"text": "predictions of intelligibility", "start": 32, "end": 62}}], "compare": [{"head": {"text": "synthetic speech", "start": 67, "end": 83}, "tail": {"text": "natural speech", "start": 120, "end": 134}}], "hyponym of": [{"head": {"text": "SII measure", "start": 155, "end": 166}, "tail": {"text": "measures", "start": 4, "end": 12}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In additional experiments , we processed the synthesized speech by an ideal binary mask before adding noise .", "output": {"relations": {"used for": [{"head": {"text": "ideal binary mask", "start": 70, "end": 87}, "tail": {"text": "synthesized speech", "start": 45, "end": 63}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The Glimpse measure gave the most accurate intelligibility predictions in this situation .", "output": {"relations": {"used for": [{"head": {"text": "Glimpse measure", "start": 4, "end": 19}, "tail": {"text": "intelligibility predictions", "start": 43, "end": 70}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "A '' graphics for vision '' approach is proposed to address the problem of reconstruction from a large and imperfect data set : reconstruction on demand by tensor voting , or ROD-TV .", "output": {"relations": {"used for": [{"head": {"text": "'' graphics for vision '' approach", "start": 2, "end": 36}, "tail": {"text": "reconstruction", "start": 75, "end": 89}}, {"head": {"text": "large and imperfect data set", "start": 97, "end": 125}, "tail": {"text": "reconstruction", "start": 75, "end": 89}}, {"head": {"text": "tensor voting", "start": 156, "end": 169}, "tail": {"text": "reconstruction", "start": 75, "end": 89}}, {"head": {"text": "ROD-TV", "start": 175, "end": 181}, "tail": {"text": "reconstruction", "start": 75, "end": 89}}], "conjunction": [{"head": {"text": "tensor voting", "start": 156, "end": 169}, "tail": {"text": "ROD-TV", "start": 175, "end": 181}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "ROD-TV simultaneously delivers good efficiency and robust-ness , by adapting to a continuum of primitive connectivity , view dependence , and levels of detail -LRB- LOD -RRB- .", "output": {"relations": {"evaluate for": [{"head": {"text": "efficiency", "start": 36, "end": 46}, "tail": {"text": "ROD-TV", "start": 0, "end": 6}}, {"head": {"text": "robust-ness", "start": 51, "end": 62}, "tail": {"text": "ROD-TV", "start": 0, "end": 6}}], "conjunction": [{"head": {"text": "robust-ness", "start": 51, "end": 62}, "tail": {"text": "efficiency", "start": 36, "end": 46}}, {"head": {"text": "view dependence", "start": 120, "end": 135}, "tail": {"text": "primitive connectivity", "start": 95, "end": 117}}, {"head": {"text": "levels of detail -LRB- LOD -RRB-", "start": 142, "end": 174}, "tail": {"text": "view dependence", "start": 120, "end": 135}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Locally inferred surface elements are robust to noise and better capture local shapes .", "output": {"relations": {"used for": [{"head": {"text": "Locally inferred surface elements", "start": 0, "end": 33}, "tail": {"text": "local shapes", "start": 73, "end": 85}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "By inferring per-vertex normals at sub-voxel precision on the fly , we can achieve interpolative shading .", "output": {"relations": {"used for": [{"head": {"text": "per-vertex normals", "start": 13, "end": 31}, "tail": {"text": "interpolative shading", "start": 83, "end": 104}}], "feature of": [{"head": {"text": "sub-voxel precision", "start": 35, "end": 54}, "tail": {"text": "per-vertex normals", "start": 13, "end": 31}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "By relaxing the mesh connectivity requirement , we extend ROD-TV and propose a simple but effective multiscale feature extraction algorithm .", "output": {"relations": {"used for": [{"head": {"text": "mesh connectivity requirement", "start": 16, "end": 45}, "tail": {"text": "multiscale feature extraction algorithm", "start": 100, "end": 139}}, {"head": {"text": "ROD-TV", "start": 58, "end": 64}, "tail": {"text": "multiscale feature extraction algorithm", "start": 100, "end": 139}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "ROD-TV consists of a hierarchical data structure that encodes different levels of detail .", "output": {"relations": {"part of": [{"head": {"text": "hierarchical data structure", "start": 21, "end": 48}, "tail": {"text": "ROD-TV", "start": 0, "end": 6}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The local reconstruction algorithm is tensor voting .", "output": {"relations": {"hyponym of": [{"head": {"text": "tensor voting", "start": 38, "end": 51}, "tail": {"text": "local reconstruction algorithm", "start": 4, "end": 34}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "It is applied on demand to the visible subset of data at a desired level of detail , by traversing the data hierarchy and collecting tensorial support in a neighborhood .", "output": {"relations": {"used for": [{"head": {"text": "traversing the data hierarchy", "start": 88, "end": 117}, "tail": {"text": "It", "start": 0, "end": 2}}, {"head": {"text": "collecting tensorial support", "start": 122, "end": 150}, "tail": {"text": "It", "start": 0, "end": 2}}], "conjunction": [{"head": {"text": "traversing the data hierarchy", "start": 88, "end": 117}, "tail": {"text": "collecting tensorial support", "start": 122, "end": 150}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Both rhetorical structure and punctuation have been helpful in discourse processing .", "output": {"relations": {"conjunction": [{"head": {"text": "rhetorical structure", "start": 5, "end": 25}, "tail": {"text": "punctuation", "start": 30, "end": 41}}], "used for": [{"head": {"text": "rhetorical structure", "start": 5, "end": 25}, "tail": {"text": "discourse processing", "start": 63, "end": 83}}, {"head": {"text": "punctuation", "start": 30, "end": 41}, "tail": {"text": "discourse processing", "start": 63, "end": 83}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Based on a corpus annotation project , this paper reports the discursive usage of 6 Chinese punctuation marks in news commentary texts : Colon , Dash , Ellipsis , Exclamation Mark , Question Mark , and Semicolon .", "output": {"relations": {"part of": [{"head": {"text": "Chinese punctuation marks", "start": 84, "end": 109}, "tail": {"text": "news commentary texts", "start": 113, "end": 134}}], "hyponym of": [{"head": {"text": "Colon", "start": 137, "end": 142}, "tail": {"text": "Chinese punctuation marks", "start": 84, "end": 109}}, {"head": {"text": "Dash", "start": 145, "end": 149}, "tail": {"text": "Chinese punctuation marks", "start": 84, "end": 109}}, {"head": {"text": "Ellipsis", "start": 152, "end": 160}, "tail": {"text": "Chinese punctuation marks", "start": 84, "end": 109}}, {"head": {"text": "Exclamation Mark", "start": 163, "end": 179}, "tail": {"text": "Chinese punctuation marks", "start": 84, "end": 109}}, {"head": {"text": "Question Mark", "start": 182, "end": 195}, "tail": {"text": "Chinese punctuation marks", "start": 84, "end": 109}}, {"head": {"text": "Semicolon", "start": 202, "end": 211}, "tail": {"text": "Chinese punctuation marks", "start": 84, "end": 109}}], "conjunction": [{"head": {"text": "Colon", "start": 137, "end": 142}, "tail": {"text": "Dash", "start": 145, "end": 149}}, {"head": {"text": "Dash", "start": 145, "end": 149}, "tail": {"text": "Ellipsis", "start": 152, "end": 160}}, {"head": {"text": "Ellipsis", "start": 152, "end": 160}, "tail": {"text": "Exclamation Mark", "start": 163, "end": 179}}, {"head": {"text": "Exclamation Mark", "start": 163, "end": 179}, "tail": {"text": "Question Mark", "start": 182, "end": 195}}, {"head": {"text": "Question Mark", "start": 182, "end": 195}, "tail": {"text": "Semicolon", "start": 202, "end": 211}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The rhetorical patterns of these marks are compared against patterns around cue phrases in general .", "output": {"relations": {"feature of": [{"head": {"text": "rhetorical patterns", "start": 4, "end": 23}, "tail": {"text": "marks", "start": 33, "end": 38}}], "compare": [{"head": {"text": "rhetorical patterns", "start": 4, "end": 23}, "tail": {"text": "patterns around cue phrases", "start": 60, "end": 87}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Results show that these Chinese punctuation marks , though fewer in number than cue phrases , are easy to identify , have strong correlation with certain relations , and can be used as distinctive indicators of nuclearity in Chinese texts .", "output": {"relations": {"compare": [{"head": {"text": "Chinese punctuation marks", "start": 24, "end": 49}, "tail": {"text": "cue phrases", "start": 80, "end": 91}}], "used for": [{"head": {"text": "Chinese punctuation marks", "start": 24, "end": 49}, "tail": {"text": "indicators of nuclearity", "start": 197, "end": 221}}], "feature of": [{"head": {"text": "Chinese texts", "start": 225, "end": 238}, "tail": {"text": "indicators of nuclearity", "start": 197, "end": 221}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The features based on Markov random field -LRB- MRF -RRB- models are usually sensitive to the rotation of image textures .", "output": {"relations": {"used for": [{"head": {"text": "Markov random field -LRB- MRF -RRB- models", "start": 22, "end": 64}, "tail": {"text": "features", "start": 4, "end": 12}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper develops an anisotropic circular Gaussian MRF -LRB- ACGMRF -RRB- model for modelling rotated image textures and retrieving rotation-invariant texture features .", "output": {"relations": {"used for": [{"head": {"text": "anisotropic circular Gaussian MRF -LRB- ACGMRF -RRB- model", "start": 23, "end": 81}, "tail": {"text": "modelling rotated image textures", "start": 86, "end": 118}}, {"head": {"text": "anisotropic circular Gaussian MRF -LRB- ACGMRF -RRB- model", "start": 23, "end": 81}, "tail": {"text": "retrieving rotation-invariant texture features", "start": 123, "end": 169}}], "conjunction": [{"head": {"text": "modelling rotated image textures", "start": 86, "end": 118}, "tail": {"text": "retrieving rotation-invariant texture features", "start": 123, "end": 169}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "To overcome the singularity problem of the least squares estimate -LRB- LSE -RRB- method , an approximate least squares estimate -LRB- ALSE -RRB- method is proposed to estimate the parameters of the ACGMRF model .", "output": {"relations": {"feature of": [{"head": {"text": "singularity problem", "start": 16, "end": 35}, "tail": {"text": "least squares estimate -LRB- LSE -RRB- method", "start": 43, "end": 88}}], "used for": [{"head": {"text": "approximate least squares estimate -LRB- ALSE -RRB- method", "start": 94, "end": 152}, "tail": {"text": "parameters of the ACGMRF model", "start": 181, "end": 211}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The rotation-invariant features can be obtained from the parameters of the ACGMRF model by the one-dimensional -LRB- 1-D -RRB- discrete Fourier transform -LRB- DFT -RRB- .", "output": {"relations": {"used for": [{"head": {"text": "parameters of the ACGMRF model", "start": 57, "end": 87}, "tail": {"text": "rotation-invariant features", "start": 4, "end": 31}}, {"head": {"text": "one-dimensional -LRB- 1-D -RRB- discrete Fourier transform -LRB- DFT -RRB-", "start": 95, "end": 169}, "tail": {"text": "rotation-invariant features", "start": 4, "end": 31}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Significantly improved accuracy can be achieved by applying the rotation-invariant features to classify SAR -LRB- synthetic aperture radar -RRB- sea ice and Brodatz imagery .", "output": {"relations": {"used for": [{"head": {"text": "rotation-invariant features", "start": 64, "end": 91}, "tail": {"text": "SAR -LRB- synthetic aperture radar", "start": 104, "end": 138}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Despite much recent progress on accurate semantic role labeling , previous work has largely used independent classifiers , possibly combined with separate label sequence models via Viterbi decoding .", "output": {"relations": {"used for": [{"head": {"text": "independent classifiers", "start": 97, "end": 120}, "tail": {"text": "semantic role labeling", "start": 41, "end": 63}}, {"head": {"text": "Viterbi decoding", "start": 181, "end": 197}, "tail": {"text": "label sequence models", "start": 155, "end": 176}}], "conjunction": [{"head": {"text": "independent classifiers", "start": 97, "end": 120}, "tail": {"text": "label sequence models", "start": 155, "end": 176}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We show how to build a joint model of argument frames , incorporating novel features that model these interactions into discriminative log-linear models .", "output": {"relations": {"part of": [{"head": {"text": "features", "start": 76, "end": 84}, "tail": {"text": "discriminative log-linear models", "start": 120, "end": 152}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This system achieves an error reduction of 22 % on all arguments and 32 % on core arguments over a state-of-the art independent classifier for gold-standard parse trees on PropBank .", "output": {"relations": {"evaluate for": [{"head": {"text": "error reduction", "start": 24, "end": 39}, "tail": {"text": "system", "start": 5, "end": 11}}, {"head": {"text": "error reduction", "start": 24, "end": 39}, "tail": {"text": "independent classifier", "start": 116, "end": 138}}, {"head": {"text": "gold-standard parse trees", "start": 143, "end": 168}, "tail": {"text": "system", "start": 5, "end": 11}}, {"head": {"text": "gold-standard parse trees", "start": 143, "end": 168}, "tail": {"text": "independent classifier", "start": 116, "end": 138}}], "compare": [{"head": {"text": "independent classifier", "start": 116, "end": 138}, "tail": {"text": "system", "start": 5, "end": 11}}], "part of": [{"head": {"text": "gold-standard parse trees", "start": 143, "end": 168}, "tail": {"text": "PropBank", "start": 172, "end": 180}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In order to deal with ambiguity , the MORphological PArser MORPA is provided with a probabilistic context-free grammar -LRB- PCFG -RRB- , i.e. it combines a `` conventional '' context-free morphological grammar to filter out ungrammatical segmentations with a probability-based scoring function which determines the likelihood of each successful parse .", "output": {"relations": {"used for": [{"head": {"text": "MORphological PArser MORPA", "start": 38, "end": 64}, "tail": {"text": "ambiguity", "start": 22, "end": 31}}, {"head": {"text": "probabilistic context-free grammar -LRB- PCFG -RRB-", "start": 84, "end": 135}, "tail": {"text": "MORphological PArser MORPA", "start": 38, "end": 64}}, {"head": {"text": "`` conventional '' context-free morphological grammar", "start": 157, "end": 210}, "tail": {"text": "it", "start": 18, "end": 20}}, {"head": {"text": "`` conventional '' context-free morphological grammar", "start": 157, "end": 210}, "tail": {"text": "ungrammatical segmentations", "start": 225, "end": 252}}, {"head": {"text": "probability-based scoring function", "start": 260, "end": 294}, "tail": {"text": "it", "start": 18, "end": 20}}, {"head": {"text": "probability-based scoring function", "start": 260, "end": 294}, "tail": {"text": "parse", "start": 346, "end": 351}}], "conjunction": [{"head": {"text": "probability-based scoring function", "start": 260, "end": 294}, "tail": {"text": "`` conventional '' context-free morphological grammar", "start": 157, "end": 210}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Test performance data will show that a PCFG yields good results in morphological parsing .", "output": {"relations": {"used for": [{"head": {"text": "PCFG", "start": 39, "end": 43}, "tail": {"text": "morphological parsing", "start": 67, "end": 88}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "MORPA is a fully implemented parser developed for use in a text-to-speech conversion system .", "output": {"relations": {"hyponym of": [{"head": {"text": "MORPA", "start": 0, "end": 5}, "tail": {"text": "parser", "start": 29, "end": 35}}], "used for": [{"head": {"text": "MORPA", "start": 0, "end": 5}, "tail": {"text": "text-to-speech conversion system", "start": 59, "end": 91}}, {"head": {"text": "parser", "start": 29, "end": 35}, "tail": {"text": "text-to-speech conversion system", "start": 59, "end": 91}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper describes the framework of a Korean phonological knowledge base system using the unification-based grammar formalism : Korean Phonology Structure Grammar -LRB- KPSG -RRB- .", "output": {"relations": {"used for": [{"head": {"text": "unification-based grammar formalism", "start": 92, "end": 127}, "tail": {"text": "Korean phonological knowledge base system", "start": 40, "end": 81}}], "hyponym of": [{"head": {"text": "Korean Phonology Structure Grammar -LRB- KPSG -RRB-", "start": 130, "end": 181}, "tail": {"text": "unification-based grammar formalism", "start": 92, "end": 127}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The approach of KPSG provides an explicit development model for constructing a computational phonological system : speech recognition and synthesis system .", "output": {"relations": {"used for": [{"head": {"text": "approach", "start": 4, "end": 12}, "tail": {"text": "KPSG", "start": 16, "end": 20}}, {"head": {"text": "KPSG", "start": 16, "end": 20}, "tail": {"text": "phonological system", "start": 93, "end": 112}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We show that the proposed approach is more describable than other approaches such as those employing a traditional generative phonological approach .", "output": {"relations": {"compare": [{"head": {"text": "approach", "start": 26, "end": 34}, "tail": {"text": "approaches", "start": 66, "end": 76}}], "used for": [{"head": {"text": "generative phonological approach", "start": 115, "end": 147}, "tail": {"text": "those", "start": 85, "end": 90}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper , we study the design of core-selecting payment rules for such domains .", "output": {"relations": {"used for": [{"head": {"text": "design of core-selecting payment rules", "start": 29, "end": 67}, "tail": {"text": "domains", "start": 77, "end": 84}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We design two core-selecting rules that always satisfy IR in expectation .", "output": {"relations": {"used for": [{"head": {"text": "core-selecting rules", "start": 14, "end": 34}, "tail": {"text": "IR", "start": 55, "end": 57}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "To study the performance of our rules we perform a computational Bayes-Nash equilibrium analysis .", "output": {"relations": {"used for": [{"head": {"text": "computational Bayes-Nash equilibrium analysis", "start": 51, "end": 96}, "tail": {"text": "rules", "start": 32, "end": 37}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We show that , in equilibrium , our new rules have better incentives , higher efficiency , and a lower rate of ex-post IR violations than standard core-selecting rules .", "output": {"relations": {"compare": [{"head": {"text": "rules", "start": 40, "end": 45}, "tail": {"text": "core-selecting rules", "start": 147, "end": 167}}], "evaluate for": [{"head": {"text": "rate of ex-post IR violations", "start": 103, "end": 132}, "tail": {"text": "rules", "start": 40, "end": 45}}, {"head": {"text": "rate of ex-post IR violations", "start": 103, "end": 132}, "tail": {"text": "core-selecting rules", "start": 147, "end": 167}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper , we will describe a search tool for a huge set of ngrams .", "output": {"relations": {"used for": [{"head": {"text": "search tool", "start": 35, "end": 46}, "tail": {"text": "ngrams", "start": 65, "end": 71}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This system can be a very useful tool for linguistic knowledge discovery and other NLP tasks .", "output": {"relations": {"used for": [{"head": {"text": "tool", "start": 33, "end": 37}, "tail": {"text": "linguistic knowledge discovery", "start": 42, "end": 72}}, {"head": {"text": "tool", "start": 33, "end": 37}, "tail": {"text": "NLP tasks", "start": 83, "end": 92}}], "conjunction": [{"head": {"text": "linguistic knowledge discovery", "start": 42, "end": 72}, "tail": {"text": "NLP tasks", "start": 83, "end": 92}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper explores the role of user modeling in such systems .", "output": {"relations": {"part of": [{"head": {"text": "user modeling", "start": 32, "end": 45}, "tail": {"text": "systems", "start": 54, "end": 61}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Since acquiring the knowledge for a user model is a fundamental problem in user modeling , a section is devoted to this topic .", "output": {"relations": {"used for": [{"head": {"text": "user model", "start": 36, "end": 46}, "tail": {"text": "user modeling", "start": 75, "end": 88}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Next , the benefits and costs of implementing a user modeling component for a system are weighed in light of several aspects of the interaction requirements that may be imposed by the system .", "output": {"relations": {"part of": [{"head": {"text": "user modeling component", "start": 48, "end": 71}, "tail": {"text": "system", "start": 78, "end": 84}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Information extraction techniques automatically create structured databases from unstructured data sources , such as the Web or newswire documents .", "output": {"relations": {"used for": [{"head": {"text": "Information extraction techniques", "start": 0, "end": 33}, "tail": {"text": "structured databases", "start": 55, "end": 75}}, {"head": {"text": "unstructured data sources", "start": 81, "end": 106}, "tail": {"text": "Information extraction techniques", "start": 0, "end": 33}}], "hyponym of": [{"head": {"text": "Web", "start": 121, "end": 124}, "tail": {"text": "unstructured data sources", "start": 81, "end": 106}}, {"head": {"text": "newswire documents", "start": 128, "end": 146}, "tail": {"text": "unstructured data sources", "start": 81, "end": 106}}], "conjunction": [{"head": {"text": "Web", "start": 121, "end": 124}, "tail": {"text": "newswire documents", "start": 128, "end": 146}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Despite the successes of these systems , accuracy will always be imperfect .", "output": {"relations": {"evaluate for": [{"head": {"text": "accuracy", "start": 41, "end": 49}, "tail": {"text": "systems", "start": 31, "end": 38}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The information extraction system we evaluate is based on a linear-chain conditional random field -LRB- CRF -RRB- , a probabilistic model which has performed well on information extraction tasks because of its ability to capture arbitrary , overlapping features of the input in a Markov model .", "output": {"relations": {"used for": [{"head": {"text": "linear-chain conditional random field -LRB- CRF -RRB-", "start": 60, "end": 113}, "tail": {"text": "information extraction system", "start": 4, "end": 33}}, {"head": {"text": "probabilistic model", "start": 118, "end": 137}, "tail": {"text": "information extraction tasks", "start": 166, "end": 194}}, {"head": {"text": "probabilistic model", "start": 118, "end": 137}, "tail": {"text": "arbitrary , overlapping features", "start": 229, "end": 261}}], "hyponym of": [{"head": {"text": "linear-chain conditional random field -LRB- CRF -RRB-", "start": 60, "end": 113}, "tail": {"text": "probabilistic model", "start": 118, "end": 137}}], "feature of": [{"head": {"text": "arbitrary , overlapping features", "start": 229, "end": 261}, "tail": {"text": "input", "start": 269, "end": 274}}], "part of": [{"head": {"text": "arbitrary , overlapping features", "start": 229, "end": 261}, "tail": {"text": "Markov model", "start": 280, "end": 292}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We implement several techniques to estimate the confidence of both extracted fields and entire multi-field records , obtaining an average precision of 98 % for retrieving correct fields and 87 % for multi-field records .", "output": {"relations": {"conjunction": [{"head": {"text": "extracted fields", "start": 67, "end": 83}, "tail": {"text": "multi-field records", "start": 95, "end": 114}}], "evaluate for": [{"head": {"text": "average precision", "start": 130, "end": 147}, "tail": {"text": "techniques", "start": 21, "end": 31}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper , we use the information redundancy in multilingual input to correct errors in machine translation and thus improve the quality of multilingual summaries .", "output": {"relations": {"used for": [{"head": {"text": "information redundancy in multilingual input", "start": 27, "end": 71}, "tail": {"text": "machine translation", "start": 93, "end": 112}}, {"head": {"text": "information redundancy in multilingual input", "start": 27, "end": 71}, "tail": {"text": "multilingual summaries", "start": 145, "end": 167}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We demonstrate how errors in the machine translations of the input Arabic documents can be corrected by identifying and generating from such redundancy , focusing on noun phrases .", "output": {"relations": {"used for": [{"head": {"text": "Arabic documents", "start": 67, "end": 83}, "tail": {"text": "machine translations", "start": 33, "end": 53}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper , we propose a new approach to generate oriented object proposals -LRB- OOPs -RRB- to reduce the detection error caused by various orientations of the object .", "output": {"relations": {"used for": [{"head": {"text": "approach", "start": 33, "end": 41}, "tail": {"text": "oriented object proposals -LRB- OOPs -RRB-", "start": 54, "end": 96}}], "evaluate for": [{"head": {"text": "detection error", "start": 111, "end": 126}, "tail": {"text": "oriented object proposals -LRB- OOPs -RRB-", "start": 54, "end": 96}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "To this end , we propose to efficiently locate object regions according to pixelwise object probability , rather than measuring the objectness from a set of sampled windows .", "output": {"relations": {"used for": [{"head": {"text": "pixelwise object probability", "start": 75, "end": 103}, "tail": {"text": "object regions", "start": 47, "end": 61}}], "compare": [{"head": {"text": "pixelwise object probability", "start": 75, "end": 103}, "tail": {"text": "objectness", "start": 132, "end": 142}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We formulate the proposal generation problem as a generative proba-bilistic model such that object proposals of different shapes -LRB- i.e. , sizes and orientations -RRB- can be produced by locating the local maximum likelihoods .", "output": {"relations": {"used for": [{"head": {"text": "generative proba-bilistic model", "start": 50, "end": 81}, "tail": {"text": "proposal generation problem", "start": 17, "end": 44}}, {"head": {"text": "local maximum likelihoods", "start": 203, "end": 228}, "tail": {"text": "object proposals", "start": 92, "end": 108}}], "feature of": [{"head": {"text": "shapes", "start": 122, "end": 128}, "tail": {"text": "object proposals", "start": 92, "end": 108}}], "hyponym of": [{"head": {"text": "sizes", "start": 142, "end": 147}, "tail": {"text": "shapes", "start": 122, "end": 128}}, {"head": {"text": "orientations", "start": 152, "end": 164}, "tail": {"text": "shapes", "start": 122, "end": 128}}], "conjunction": [{"head": {"text": "sizes", "start": 142, "end": 147}, "tail": {"text": "orientations", "start": 152, "end": 164}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "First , it helps the object detector handle objects of different orientations .", "output": {"relations": {"used for": [{"head": {"text": "object detector", "start": 21, "end": 36}, "tail": {"text": "orientations", "start": 65, "end": 77}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Third , it avoids massive window sampling , and thereby reducing the number of proposals while maintaining a high recall .", "output": {"relations": {"used for": [{"head": {"text": "it", "start": 8, "end": 10}, "tail": {"text": "number of proposals", "start": 69, "end": 88}}], "evaluate for": [{"head": {"text": "recall", "start": 114, "end": 120}, "tail": {"text": "it", "start": 8, "end": 10}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Experiments on the PASCAL VOC 2007 dataset show that the proposed OOP outperforms the state-of-the-art fast methods .", "output": {"relations": {"evaluate for": [{"head": {"text": "PASCAL VOC 2007 dataset", "start": 19, "end": 42}, "tail": {"text": "OOP", "start": 66, "end": 69}}], "compare": [{"head": {"text": "OOP", "start": 66, "end": 69}, "tail": {"text": "state-of-the-art fast methods", "start": 86, "end": 115}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Further experiments show that the rotation invariant property helps a class-specific object detector achieve better performance than the state-of-the-art proposal generation methods in either object rotation scenarios or general scenarios .", "output": {"relations": {"used for": [{"head": {"text": "rotation invariant property", "start": 34, "end": 61}, "tail": {"text": "class-specific object detector", "start": 70, "end": 100}}], "compare": [{"head": {"text": "class-specific object detector", "start": 70, "end": 100}, "tail": {"text": "proposal generation methods", "start": 154, "end": 181}}], "evaluate for": [{"head": {"text": "object rotation scenarios", "start": 192, "end": 217}, "tail": {"text": "class-specific object detector", "start": 70, "end": 100}}, {"head": {"text": "object rotation scenarios", "start": 192, "end": 217}, "tail": {"text": "proposal generation methods", "start": 154, "end": 181}}, {"head": {"text": "general scenarios", "start": 221, "end": 238}, "tail": {"text": "class-specific object detector", "start": 70, "end": 100}}, {"head": {"text": "general scenarios", "start": 221, "end": 238}, "tail": {"text": "proposal generation methods", "start": 154, "end": 181}}], "conjunction": [{"head": {"text": "object rotation scenarios", "start": 192, "end": 217}, "tail": {"text": "general scenarios", "start": 221, "end": 238}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper describes three relatively domain-independent capabilities recently added to the Paramax spoken language understanding system : non-monotonic reasoning , implicit reference resolution , and database query paraphrase .", "output": {"relations": {"part of": [{"head": {"text": "domain-independent capabilities", "start": 38, "end": 69}, "tail": {"text": "Paramax spoken language understanding system", "start": 92, "end": 136}}], "hyponym of": [{"head": {"text": "non-monotonic reasoning", "start": 139, "end": 162}, "tail": {"text": "domain-independent capabilities", "start": 38, "end": 69}}, {"head": {"text": "implicit reference resolution", "start": 165, "end": 194}, "tail": {"text": "domain-independent capabilities", "start": 38, "end": 69}}, {"head": {"text": "database query paraphrase", "start": 201, "end": 226}, "tail": {"text": "domain-independent capabilities", "start": 38, "end": 69}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Finally , we briefly describe an experiment which we have done in extending the n-best speech/language integration architecture to improving OCR accuracy .", "output": {"relations": {"evaluate for": [{"head": {"text": "OCR accuracy", "start": 141, "end": 153}, "tail": {"text": "n-best speech/language integration architecture", "start": 80, "end": 127}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We investigate the problem of fine-grained sketch-based image retrieval -LRB- SBIR -RRB- , where free-hand human sketches are used as queries to perform instance-level retrieval of images .", "output": {"relations": {"used for": [{"head": {"text": "free-hand human sketches", "start": 97, "end": 121}, "tail": {"text": "instance-level retrieval of images", "start": 153, "end": 187}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This is an extremely challenging task because -LRB- i -RRB- visual comparisons not only need to be fine-grained but also executed cross-domain , -LRB- ii -RRB- free-hand -LRB- finger -RRB- sketches are highly abstract , making fine-grained matching harder , and most importantly -LRB- iii -RRB- annotated cross-domain sketch-photo datasets required for training are scarce , challenging many state-of-the-art machine learning techniques .", "output": {"relations": {"used for": [{"head": {"text": "annotated cross-domain sketch-photo datasets", "start": 295, "end": 339}, "tail": {"text": "machine learning techniques", "start": 409, "end": 436}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We then develop a deep triplet-ranking model for instance-level SBIR with a novel data augmentation and staged pre-training strategy to alleviate the issue of insufficient fine-grained training data .", "output": {"relations": {"used for": [{"head": {"text": "deep triplet-ranking model", "start": 18, "end": 44}, "tail": {"text": "instance-level SBIR", "start": 49, "end": 68}}, {"head": {"text": "deep triplet-ranking model", "start": 18, "end": 44}, "tail": {"text": "insufficient fine-grained training data", "start": 159, "end": 198}}, {"head": {"text": "data augmentation", "start": 82, "end": 99}, "tail": {"text": "deep triplet-ranking model", "start": 18, "end": 44}}, {"head": {"text": "staged pre-training strategy", "start": 104, "end": 132}, "tail": {"text": "deep triplet-ranking model", "start": 18, "end": 44}}], "conjunction": [{"head": {"text": "data augmentation", "start": 82, "end": 99}, "tail": {"text": "staged pre-training strategy", "start": 104, "end": 132}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Extensive experiments are carried out to contribute a variety of insights into the challenges of data sufficiency and over-fitting avoidance when training deep networks for fine-grained cross-domain ranking tasks .", "output": {"relations": {"conjunction": [{"head": {"text": "data sufficiency", "start": 97, "end": 113}, "tail": {"text": "over-fitting avoidance", "start": 118, "end": 140}}], "used for": [{"head": {"text": "deep networks", "start": 155, "end": 168}, "tail": {"text": "fine-grained cross-domain ranking tasks", "start": 173, "end": 212}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper we target at generating generic action proposals in unconstrained videos .", "output": {"relations": {"used for": [{"head": {"text": "unconstrained videos", "start": 66, "end": 86}, "tail": {"text": "generic action proposals", "start": 38, "end": 62}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Each action proposal corresponds to a temporal series of spatial bounding boxes , i.e. , a spatio-temporal video tube , which has a good potential to locate one human action .", "output": {"relations": {"hyponym of": [{"head": {"text": "spatio-temporal video tube", "start": 91, "end": 117}, "tail": {"text": "temporal series of spatial bounding boxes", "start": 38, "end": 79}}], "used for": [{"head": {"text": "spatio-temporal video tube", "start": 91, "end": 117}, "tail": {"text": "human action", "start": 161, "end": 173}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Assuming each action is performed by a human with meaningful motion , both appearance and motion cues are utilized to measure the ac-tionness of the video tubes .", "output": {"relations": {"used for": [{"head": {"text": "appearance and motion cues", "start": 75, "end": 101}, "tail": {"text": "ac-tionness", "start": 130, "end": 141}}], "evaluate for": [{"head": {"text": "ac-tionness", "start": 130, "end": 141}, "tail": {"text": "video tubes", "start": 149, "end": 160}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "After picking those spatiotem-poral paths of high actionness scores , our action proposal generation is formulated as a maximum set coverage problem , where greedy search is performed to select a set of action proposals that can maximize the overall actionness score .", "output": {"relations": {"used for": [{"head": {"text": "maximum set coverage problem", "start": 120, "end": 148}, "tail": {"text": "action proposal generation", "start": 74, "end": 100}}, {"head": {"text": "greedy search", "start": 157, "end": 170}, "tail": {"text": "action proposals", "start": 203, "end": 219}}], "evaluate for": [{"head": {"text": "actionness score", "start": 50, "end": 66}, "tail": {"text": "action proposals", "start": 203, "end": 219}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Compared with existing action proposal approaches , our action proposals do not rely on video segmentation and can be generated in nearly real-time .", "output": {"relations": {"compare": [{"head": {"text": "action proposal approaches", "start": 23, "end": 49}, "tail": {"text": "action proposals", "start": 56, "end": 72}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Experimental results on two challenging datasets , MSRII and UCF 101 , validate the superior performance of our action proposals as well as competitive results on action detection and search .", "output": {"relations": {"evaluate for": [{"head": {"text": "datasets", "start": 40, "end": 48}, "tail": {"text": "action proposals", "start": 112, "end": 128}}, {"head": {"text": "action detection and search", "start": 163, "end": 190}, "tail": {"text": "action proposals", "start": 112, "end": 128}}], "hyponym of": [{"head": {"text": "MSRII", "start": 51, "end": 56}, "tail": {"text": "datasets", "start": 40, "end": 48}}, {"head": {"text": "UCF 101", "start": 61, "end": 68}, "tail": {"text": "datasets", "start": 40, "end": 48}}], "conjunction": [{"head": {"text": "MSRII", "start": 51, "end": 56}, "tail": {"text": "UCF 101", "start": 61, "end": 68}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper reports recent research into methods for creating natural language text .", "output": {"relations": {"used for": [{"head": {"text": "methods", "start": 40, "end": 47}, "tail": {"text": "creating natural language text", "start": 52, "end": 82}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "KDS -LRB- Knowledge Delivery System -RRB- , which embodies this paradigm , has distinct parts devoted to creation of the propositional units , to organization of the text , to prevention of excess redundancy , to creation of combinations of units , to evaluation of these combinations as potential sentences , to selection of the best among competing combinations , and to creation of the final text .", "output": {"relations": {"part of": [{"head": {"text": "paradigm", "start": 64, "end": 72}, "tail": {"text": "KDS -LRB- Knowledge Delivery System -RRB-", "start": 0, "end": 41}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The Fragment-and-Compose paradigm and the computational methods of KDS are described .", "output": {"relations": {"used for": [{"head": {"text": "computational methods", "start": 42, "end": 63}, "tail": {"text": "KDS", "start": 67, "end": 70}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper explores the issue of using different co-occurrence similarities between terms for separating query terms that are useful for retrieval from those that are harmful .", "output": {"relations": {"used for": [{"head": {"text": "co-occurrence similarities", "start": 49, "end": 75}, "tail": {"text": "query terms", "start": 105, "end": 116}}, {"head": {"text": "query terms", "start": 105, "end": 116}, "tail": {"text": "retrieval", "start": 137, "end": 146}}], "compare": [{"head": {"text": "those", "start": 152, "end": 157}, "tail": {"text": "query terms", "start": 105, "end": 116}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The hypothesis under examination is that useful terms tend to be more similar to each other than to other query terms .", "output": {"relations": {"compare": [{"head": {"text": "useful terms", "start": 41, "end": 53}, "tail": {"text": "query terms", "start": 106, "end": 117}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Preliminary experiments with similarities computed using first-order and second-order co-occurrence seem to confirm the hypothesis .", "output": {"relations": {"used for": [{"head": {"text": "first-order and second-order co-occurrence", "start": 57, "end": 99}, "tail": {"text": "similarities", "start": 29, "end": 41}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We propose a new phrase-based translation model and decoding algorithm that enables us to evaluate and compare several , previously proposed phrase-based translation models .", "output": {"relations": {"conjunction": [{"head": {"text": "phrase-based translation model", "start": 17, "end": 47}, "tail": {"text": "decoding algorithm", "start": 52, "end": 70}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Within our framework , we carry out a large number of experiments to understand better and explain why phrase-based models outperform word-based models .", "output": {"relations": {"compare": [{"head": {"text": "phrase-based models", "start": 103, "end": 122}, "tail": {"text": "word-based models", "start": 134, "end": 151}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Our empirical results , which hold for all examined language pairs , suggest that the highest levels of performance can be obtained through relatively simple means : heuristic learning of phrase translations from word-based alignments and lexical weighting of phrase translations .", "output": {"relations": {"hyponym of": [{"head": {"text": "heuristic learning of phrase translations", "start": 166, "end": 207}, "tail": {"text": "means", "start": 158, "end": 163}}, {"head": {"text": "lexical weighting of phrase translations", "start": 239, "end": 279}, "tail": {"text": "means", "start": 158, "end": 163}}], "used for": [{"head": {"text": "word-based alignments", "start": 213, "end": 234}, "tail": {"text": "heuristic learning of phrase translations", "start": 166, "end": 207}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Traditional methods for color constancy can improve surface re-flectance estimates from such uncalibrated images , but their output depends significantly on the background scene .", "output": {"relations": {"used for": [{"head": {"text": "methods", "start": 12, "end": 19}, "tail": {"text": "color constancy", "start": 24, "end": 39}}, {"head": {"text": "methods", "start": 12, "end": 19}, "tail": {"text": "surface re-flectance estimates", "start": 52, "end": 82}}, {"head": {"text": "uncalibrated images", "start": 93, "end": 112}, "tail": {"text": "surface re-flectance estimates", "start": 52, "end": 82}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We introduce the multi-view color constancy problem , and present a method to recover estimates of underlying surface re-flectance based on joint estimation of these surface properties and the illuminants present in multiple images .", "output": {"relations": {"used for": [{"head": {"text": "method", "start": 68, "end": 74}, "tail": {"text": "estimates of underlying surface re-flectance", "start": 86, "end": 130}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The method can exploit image correspondences obtained by various alignment techniques , and we show examples based on matching local region features .", "output": {"relations": {"used for": [{"head": {"text": "method", "start": 4, "end": 10}, "tail": {"text": "image correspondences", "start": 23, "end": 44}}, {"head": {"text": "alignment techniques", "start": 65, "end": 85}, "tail": {"text": "image correspondences", "start": 23, "end": 44}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Our results show that multi-view constraints can significantly improve estimates of both scene illuminants and object color -LRB- surface reflectance -RRB- when compared to a baseline single-view method .", "output": {"relations": {"used for": [{"head": {"text": "multi-view constraints", "start": 22, "end": 44}, "tail": {"text": "estimates of both scene illuminants and object color -LRB- surface reflectance -RRB-", "start": 71, "end": 155}}], "compare": [{"head": {"text": "baseline single-view method", "start": 175, "end": 202}, "tail": {"text": "multi-view constraints", "start": 22, "end": 44}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Our contributions include a concise , modular architecture with reversible processes of understanding and generation , an information-state model of reference , and flexible links between semantics and collaborative problem solving .", "output": {"relations": {"used for": [{"head": {"text": "concise , modular architecture", "start": 28, "end": 58}, "tail": {"text": "understanding", "start": 88, "end": 101}}, {"head": {"text": "concise , modular architecture", "start": 28, "end": 58}, "tail": {"text": "generation", "start": 106, "end": 116}}], "conjunction": [{"head": {"text": "understanding", "start": 88, "end": 101}, "tail": {"text": "generation", "start": 106, "end": 116}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}