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README.md

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+# Unified Benchmark for Metaphor Identification (UBMI)
+
+The **Unified Benchmark for Metaphor Identification (UBMI)** gathers a wide range of
+metaphor and idiomaticity datasets into a single, harmonised format. The collected
+datasets were not all created for the automatic identification of metaphors: some come
+from psycholinguistic studies designed to collect human ratings or neurological
+measurements (e.g. CARD, JANK), some were originally created in other languages and
+later manually translated by their authors (e.g. WANG, BAMB), some are collections of
+metaphors obtained with concordancers (e.g. CCM), and others are full documents annotated
+to provide a standard annotation scheme (e.g. VUAC). Even within NLP, the datasets serve
+varied purposes — studying compositionality (GUT), evaluating metaphor generation (CHAK),
+analysing metaphoricity (DUNN), or disambiguating potentially idiomatic expressions.
+
+Because of these differences, UBMI is **not** designed to merge all labelled phrases into
+one large dataset. What the collected datasets have in common is that they encode the
+metaphoric or idiomatic properties of expressions in context — or, conversely, their
+absence. UBMI re-encodes them with unified field names to facilitate experiments and
+comparisons across this diversity. In the open benchmark, only datasets with open licences
+are included.
+
+---
+
+## Loading the dataset
+
+Each source dataset is exposed as a separate **subset (config)**, named
+`<dataset>_<method>`, where `<method>` is one of `full`, `lexical` or `random`:
+
+- **`random`** and **`lexical`** subsets have `train` / `validation` / `test` splits.
+- **`full`** subsets are the whole dataset merged into a single split named `full`
+  (no train/test separation — useful as a reference set, but do **not** train on a
+  `full` subset and then evaluate on the matching `random`/`lexical` test split).
+
+```python
+from datasets import load_dataset, get_dataset_config_names
+import json
+
+# See every available subset name
+configs = get_dataset_config_names("USERNAME/UBMI")
+
+# Load one subset
+ds = load_dataset("USERNAME/UBMI", "TroFi_random")
+example = ds["test"][0]
+
+# Dataset-specific / optional fields are stored as a JSON string — parse them:
+extra = json.loads(example["additional_information"])
+print(extra["lemma"], extra["pos"])
+```
+
+> **Note on `additional_information`.** Across the benchmark this field holds ~80 different
+> keys with mixed and sometimes incompatible types. To keep every subset loadable with a
+> single, stable schema, it is stored as a **JSON-encoded string**. Decode it with
+> `json.loads(...)` to recover the original nested object.
+
+> **Config names vs. abbreviations.** Config names use each dataset's internal id, which
+> occasionally differs from the abbreviation in the table below (e.g. `TSVET_AN` for
+> TSV<sub>A</sub>, `VUAC_MW` for VUAC<sub>BO</sub>, `SE2013_ALL_WORDS` for SE2013<sub>ALL</sub>).
+> Use `get_dataset_config_names(...)` to list the exact names.
+
+---
+
+## One UBMI entry
+
+Every example is stored with a unique tagged Potentially Metaphoric Expression (PME) per
+sentence, together with the minimal information needed for a binary classification task
+(the position of the expression in its context and its label), plus optional unified and
+dataset-specific fields kept under `additional_information`.
+
+![Example of one UBMI entry from the CARD_N dataset](media/one-ubmi-example.png)
+
+*An example UBMI entry from the CARD\_N dataset (Cardillo et al., 2010). The core fields
+(`ref_id`, `dataset_id`, `id`, `context`, `pme`, `position`, `label`, `split`) locate and
+label the PME, while `additional_information` preserves unified optional fields (e.g.
+`lemma`, `pos`, `mscore`, `tenor`, `tenor_position`, `set_id`, five-fold splits) and the
+original dataset-specific ratings.*
+
+### Top-level fields (columns)
+
+| Field | Description |
+|---|---|
+| `ref_id` | Name of the original dataset (first author's abbreviated name if no other name exists). Several UBMI datasets may come from the same reference dataset. |
+| `dataset_id` | Unique id for a UBMI dataset, often composed of the reference id and a split (random, lexical, etc.). |
+| `id` | Entry id for a single example. |
+| `context` | Text containing the PME (most often a sentence). |
+| `pme` | The Potentially Metaphoric Expression: the labelled words in the context. |
+| `position` | Offset `[start, end]` of the labelled expression within `context`. |
+| `label` | Only *metaphorical* (`m`), *literal* (`l`) and *other* (`o`) labels appear here; finer labels are kept in `original_label` inside `additional_information`. |
+| `split` | Whether the example belongs to the train, validation or test set (present in `lexical` / `random` subsets). |
+| `data_split` | The sampling method — random, lexical, original split, etc. (present in `lexical` / `random` subsets). |
+| `additional_information` | **JSON-encoded string** holding the fields below plus any non-unified, dataset-specific fields. |
+
+### Inside `additional_information` (after `json.loads`)
+
+| Field | Description |
+|---|---|
+| `lemma` | Lemma(s) of the PME, obtained with spaCy. |
+| `pos` | A single PoS, or a list of PoS for PMEs longer than one word. |
+| `pos_pattern` | The PoS pattern of the PME. |
+| `five_folds` | Train/validation/test membership across 5 folds (for cross-validation). |
+| `long_context` | Additional context when the original dataset provides more than one sentence. |
+| `mscore` | Metaphoricity or confidence score scaled to [0, 1]. |
+| `tenor` / `tenor_lemma` / `tenor_pos` / `tenor_position` | The topic word(s) of the metaphor and their lemma, PoS and position. |
+| `source_domain` / `target_domain` | Conceptual source-to-target domain mapping. |
+| `original_label` | The original (usually more fine-grained) label when it differs from the binary label. |
+| `set_id` | Present when the sentence is grouped with others (usually pairs or triples). |
+| *other* | Non-unified fields are stored here as *original dataset information*. |
+
+---
+
+## Splits
+
+All datasets containing both literal and metaphorical labels are split into **training
+(70%)**, **development (10%)** and **test (20%)** sets. Collections of metaphors without
+literal examples are not split. Splits come in two main sampling methods:
+
+- **Random splits** — simple random shuffles of the data.
+- **Lexical splits** — ensure a PME present in one split does not appear in the others,
+  probing model generalisation.
+
+Some datasets contain duplicated sentences (e.g. MIPVU datasets) or duplicated contexts
+where only the tagged expression changes (e.g. CHAK, GUT, TONG); for these, additional
+**context-based splits** keep the same context out of more than one split. Fixed
+**5-fold cross-validation** splits are provided for both random and lexical sampling, and
+any original split shipped with a dataset is preserved.
+
+---
+
+## UBMI datasets
+
+The table below lists all datasets in the open benchmark, with the number of entries,
+number/percentage of metaphors, number of distinct expressions and contexts, the context
+span, and the sizes of the random-split train/dev/test sets.
+The **Available?** column indicates whether the dataset is already part of this release
+(✅ Available) or will be added shortly (⏳ Soon).
+
+| Dataset Name | Available? | N Entry | N Met (%) | N dist. Expr. | N dist. Ctxt. | Ctxt. Span | Train | Dev | Test |
+|---|---|---|---|---|---|---|---|---|---|
+| **Nominal PME** | | | | | | | | | |
+| CARD<sub>N</sub> | ✅ Available | 512 | 256 (50) | 256 | 511 | sentence | 358 | 51 | 103 |
+| JANK | ✅ Available | 360 | 120 (33) | 120 | 360 | sentence | 252 | 36 | 72 |
+| BAMB | ⏳ Soon | 115 | 115 (100) | 91 | 84 | noun | 0 | 0 | 115 |
+| WANG | ⏳ Soon | 240 | 120 (50) | 120 | 240 | sentence | 128 | 24 | 48 |
+| Fig-QA | ✅ Available | 8922 | 8922 (100) | 6845 | 4454 | sentence | 0 | 0 | 8922 |
+| 2x2Meta | ⏳ Soon | 691 | 329 (48) | 547 | 203 | paragraph | 484 | 69 | 138 |
+| **Adjectival PME** | | | | | | | | | |
+| GUT | ✅ Available | 8591 | 4601 (54) | 23 | 3479 | noun | 6014 | 859 | 1718 |
+| NEU | ✅ Available | 100 | 56 (56) | 5 | 93 | noun | 70 | 10 | 20 |
+| TSV<sub>A</sub> | ✅ Available | 1945 | 979 (50) | 687 | 1072 | noun | 1362 | 195 | 388 |
+| **Verbal PME** | | | | | | | | | |
+| CARD<sub>V</sub> | ✅ Available | 280 | 140 (50) | 140 | 280 | sentence | 196 | 28 | 56 |
+| CHAK | ✅ Available | 468 | 312 (67) | 355 | 155 | sentence | 328 | 47 | 93 |
+| MOH | ✅ Available | 1632 | 407 (25) | 438 | 1630 | sentence | 1142 | 163 | 327 |
+| DUNN | ✅ Available | 60 | 40 (67) | 20 | 60 | sentence | 0 | 0 | 100 |
+| TSV<sub>V</sub> | ⏳ Soon | 222 | 111 (50) | 119 | 222 | sentence | 0 | 0 | 222 |
+| TroFi | ✅ Available | 3642 | 2098 (58) | 50 | 3642 | sentence | 2549 | 364 | 729 |
+| NewsMet | ⏳ Soon | 1205 | 594 (49) | 680 | 1205 | sentence | 844 | 120 | 241 |
+| **Various** | | | | | | | | | |
+| TONG | ⏳ Soon | 1428 | 655 (46) | 1031 | 739 | sentence | 1000 | 142 | 286 |
+| CCM | ✅ Available | 8492 | 8492 (100) | 590 | 8490 | sentence | 0 | 0 | 8492 |
+| GORD | ⏳ Soon | 1771 | 1771 (100) | 574 | 1771 | sentence | 0 | 0 | 1771 |
+| ATT-META-S | ⏳ Soon | 500 | 500 (100) | 500 | 500 | sentence | 0 | 0 | 500 |
+| **PIEs** | | | | | | | | | |
+| VNC | ✅ Available | 2568 | 2017 (79) | 53 | 2534 | 3 sentences | 1798 | 257 | 513 |
+| PVC | ✅ Available | 1348 | 878 (65) | 23 | 1348 | 3 sentences | 944 | 135 | 269 |
+| SE2013<sub>ALL</sub> | ✅ Available | 1969 | 1172 (60) | 55 | 1939 | 3 sentences | 1378 | 197 | 394 |
+| SE2013<sub>LEX</sub> | ✅ Available | 2371 | 1199 (51) | 10 | 2339 | 3 sentences | 1660 | 237 | 474 |
+| PIE | ✅ Available | 3025 | 1434 (47) | 1608 | 2204 | 3 sentences | 2118 | 303 | 604 |
+| MAD | ✅ Available | 4558 | 2190 (48) | 443 | 4554 | 3 sentences | 3191 | 456 | 911 |
+| MAGPIE | ✅ Available | 48395 | 36328 (75) | 9462 | 47280 | 5 sentences | 33876 | 4839 | 9680 |
+| PARSEME | ⏳ Soon | 1114 | 1114 (100) | 530 | 958 | sentence | 0 | 0 | 1114 |
+| **MIPVU** | | | | | | | | | |
+| VUAC<sub>BO</sub> | ✅ Available | 39223 | 20350 (52) | 8722 | 39108 | 3 sentences | 27456 | 3922 | 7845 |
+| VUAC<sub>ST1</sub> | ✅ Available | 23113 | 6554 (28) | 2382 | 22894 | 3 sentences | 16179 | 2311 | 4623 |
+| VUAC<sub>ST2</sub> | ✅ Available | 94807 | 15026 (16) | 13509 | 93382 | 3 sentences | 66365 | 9481 | 18961 |
+| NACEY | ⏳ Soon | 498 | 249 (50) | 244 | 498 | 3 sentences | 349 | 49 | 100 |
+| JUL | ⏳ Soon | 4268 | 2134 (50) | 1649 | 4268 | 3 sentences | 2988 | 426 | 854 |
+
+---
+
+## Dataset descriptions by category
+
+The datasets are grouped along two dimensions: the syntactic form of the labelled
+expression, and the granularity of the annotation. The first three groups are organised by
+syntactic form (nominal; adjectival and verbal; mixed), and the last two by the type of
+expression and annotation procedure (multi-word expressions; MIPVU). Each table below lists
+the UBMI datasets of that category with their original reference and original licence.
+
+### Nominal metaphors
+
+Datasets designed for the study of the metaphorical usage of nouns or noun phrases (NPs).
+The figurative examples are **direct metaphors**, where the tenor NP and the vehicle NP are
+explicitly related within the sentence (e.g. *Man is a wolf*, *an ocean of happiness*).
+Most were created for psycholinguistic or neurolinguistic studies; two come from NLP
+projects.
+
+| Dataset | Reference | Original licence |
+|---|---|---|
+| CARD<sub>N</sub> | Cardillo et al. (2010); Cardillo et al. (2017) | CC BY-NC |
+| JANK | Jankowiak (2020) | CC BY 4.0 |
+| BAMB | Bambini et al. (2014) | CC BY 4.0 |
+| WANG | Wang et al. — Chinese metaphor norms | Open |
+| Fig-QA | Liu et al. (2022) | MIT License — copy, modification and redistribution allowed; must always include the original license: https://github.com/nightingal3/Fig-QA/blob/master/LICENSE |
+| 2x2Meta | Boisson et al. (2025) | CC BY-NC |
+
+### Adjectival and verbal (indirect) metaphors
+
+**Indirect metaphors** — the source concept is suggested by the words used metaphorically
+but not explicitly named (e.g. *tasty metaphor*, *pour money*, *economy flourishes*). They
+are the most frequent type of metaphor in natural text, and the datasets were created in
+majority for NLP studies. Adjectival datasets annotate the literal/metaphorical use of an
+adjective in the context of a noun (often adjective-noun pairs); verbal datasets are
+collections of full sentences.
+
+**Adjectival PME**
+
+| Dataset | Reference | Original licence |
+|---|---|---|
+| GUT | Gutiérrez et al. (2016) | AFL-3.0 |
+| NEU | Turney et al. (2011); Assaf et al. (2013) | CC BY-NC 4.0 |
+| TSV<sub>A</sub> | Tsvetkov et al. (2014) | Redistribution allowed; license at https://github.com/ytsvetko/metaphor/blob/master/LICENSE.md |
+
+**Verbal PME**
+
+| Dataset | Reference | Original licence |
+|---|---|---|
+| CARD<sub>V</sub> | Cardillo et al. (2010); Cardillo et al. (2017) | CC BY-NC |
+| CHAK | Chakrabarty et al. (2021) | No explicit licence; original data shared on GitHub: https://github.com/tuhinjubcse/MetaphorGenNAACL2021 |
+| MOH | Mohammad et al. (2016) | Redistribution allowed; license at https://saifmohammad.com/WebPages/metaphor.html |
+| DUNN | Dunn (2014) | CC BY-SA 3.0 |
+| TSV<sub>V</sub> | Tsvetkov et al. (2014) | Redistribution allowed; license at https://github.com/ytsvetko/metaphor/blob/master/LICENSE.md |
+| TroFi | Birke & Sarkar (2006) | CC BY-NC 4.0 |
+| NewsMet | Joseph et al. (2023) | Apache-2.0 |
+
+### Mixed metaphors
+
+Datasets labelled with **various types of metaphoric expressions**, with (mostly) no
+syntactic constraint on the context or the PME, and expressions that may span more than one
+word. They include collections of metaphors focused on mapping analysis and datasets built
+for metaphor identification. (In the UBMI table these appear under the **Various** group.)
+
+| Dataset | Reference | Original licence |
+|---|---|---|
+| TONG | Tong et al. (2024) | CC BY 4.0 |
+| CCM | MacWhinney & Fromm (2014); Levin et al. (2014) | CC BY-NC 4.0 |
+| GORD | Gordon et al. (2015) | Redistribution allowed with required attribution. Any product, report, publication, presentation or document including or referencing the data must contain the text: *"This effort contains or makes use of the IARPA-funded Metaphor Program USC/ISI annotated metaphorical language collection, release iarpa_metaphor_isi.edu_metaphor_corpus_20150403"* |
+| ATT-META-S | ATT-Meta Databank — Barnden et al. | Non-commercial research/instructional use; most pages of the databank may be freely copied for that purpose: https://www.cs.nmsu.edu/atmet/Databank/root.html.OLD |
+
+### Multi-Word Expressions (MWE / PIEs)
+
+Datasets where all labelled expressions are **multi-word expressions**. Most are
+**Potentially Idiomatic Expressions (PIEs)** — the same surface form occurs both literally
+and idiomatically — compiled and annotated for binary classification. Some target MWEs with
+specific patterns (noun compounds, verb-preposition compounds); others impose no
+restriction on the compound form. PARSEME 1.3 is an exception: it was created for verbal-MWE
+identification (negative cases are not annotated by default). (In the UBMI table these
+appear under the **PIEs** group.)
+
+| Dataset | Reference | Original licence |
+|---|---|---|
+| VNC | Cook et al. (2008) | No specific licence stated (no entry in the licence appendix) |
+| PVC | Tu & Roth (2012) | No explicit licence; original dataset: https://cogcomp.seas.upenn.edu/page/resource_view/26 |
+| SE2013<sub>ALL</sub> | Korkontzelos et al. (2013) | CC BY-SA 4.0; redistribution authorised directly by the authors. Original download: https://www.inf.uni-hamburg.de/en/inst/ab/lt/resources/data.html |
+| SE2013<sub>LEX</sub> | Korkontzelos et al. (2013) | CC BY-SA 4.0; redistribution authorised directly by the authors. Original download: https://www.inf.uni-hamburg.de/en/inst/ab/lt/resources/data.html |
+| PIE | Haagsma et al. (2019) | CC BY 4.0 |
+| MAD | Tayyar Madabushi et al. (2021, 2022) | GPL-3.0 |
+| MAGPIE | Haagsma et al. (2020) | CC BY 4.0 |
+| PARSEME | Savary et al. (2023) | CC BY-SA 4.0 / CC BY-SA 3.0 / CC BY 4.0 |
+
+### MIPVU datasets
+
+Datasets annotated with the **MIPVU** procedure (and its extensions): richly annotated
+documents in which each lexical unit is labelled metaphoric or literal. The benchmark
+retains three open-source MIPVU datasets (VUAC, NACEY, JUL); the VUAC is additionally
+sampled and reformatted for binary classification — balanced multi-word sampling
+(VUAC<sub>BO</sub>) and the shared-task samplings (VUAC<sub>ST1</sub>, VUAC<sub>ST2</sub>).
+The licence shown for the VUAC samplings is that of the underlying VUAC corpus.
+
+| Dataset | Reference | Original licence |
+|---|---|---|
+| VUAC<sub>BO</sub> | Boisson et al. (2023) sampling of Steen et al. (2010) — VUAC | CC BY-SA 3.0 |
+| VUAC<sub>ST1</sub> | Leong et al. (2020) sampling of Steen et al. (2010) — VUAC | CC BY-SA 3.0 |
+| VUAC<sub>ST2</sub> | Leong et al. (2020) sampling of Steen et al. (2010) — VUAC | CC BY-SA 3.0 |
+| NACEY | Nacey (2019) | CC0 1.0 |
+| JUL | Julich (2022) | CC BY-SA 4.0 |
+
+---
+
+## Licensing
+
+UBMI aggregates datasets released under **different original licences** (listed per dataset
+above); only datasets with open licences are included in this benchmark. The repository's
+`license: other` tag reflects this mixture — please consult and respect each dataset's
+individual licence for any redistribution or derivative use.
+
+## Citation
+
+<!-- Add your paper/citation here once available. -->
+
+```
+@misc{ubmi,
+  title  = {Unified Benchmark for Metaphor Identification (UBMI)},
+  author = {...},
+  year   = {2025}
+}
+```
 ---
 pretty_name: Unified Benchmark for Metaphor Identification (UBMI)
 license: other