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5d9ce87 77e1b89 5d9ce87 77e1b89 5d9ce87 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 | # Denotational Type Classifier
This model classifies the denotational relation between two word senses, represented as a pair of definitions. It is based on `roberta-large` with a sequence-classification head and is trained for lexical-semantic relation classification.
The model predicts one of the following relation types:
* `generalization`
* `specialization`
* `metaphor`
* `metonymy`
* `homonymy`
* `antonymy`
The first five labels correspond to the denotational relation types evaluated in SenseRel. `antonymy` is included because it is present in the fine-tuning data, although it is not part of the expert-annotated SenseRel denotational test set.
## Model Details
* **Model type:** RoBERTa-large sequence classifier
* **Base model:** `roberta-large`
* **Task:** Denotational relation classification between word-sense definitions
* **Input:** Two sense definitions for the same lexical item
* **Output:** A denotational relation label
* **Language:** English
## Intended Use
This model is intended for research on lexical semantics, semantic change, polysemy, and sense-level semantic relations. It can be used to classify the relation between two definitions of a word sense, for example whether a newer meaning is a generalization, specialization, metaphorical extension, metonymic extension, homonym, or antonymic development of another meaning.
Example use cases include:
* studying semantic change in dictionaries or lexical resources;
* analyzing polysemous word senses;
* supporting sense-level semantic annotation;
* scaling exploratory studies of denotational change.
The model is not intended for high-stakes decision-making or for general-purpose natural language understanding outside lexical-semantic relation classification.
## Input Format
The model expects a pair of definitions. In the experiments, definitions were concatenated and passed to the classifier.
Example:
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
model_id = "ChangeIsKey/denotational-roberta-classifier"
classifier = pipeline(
"text-classification",
model=model_id,
tokenizer=model_id
)
definition_1 = "the young of the domestic cow"
definition_2 = "the young of various large mammals"
text = definition_1 + " </s></s> " + definition_2
classifier(text)
```
Expected output:
```python
[{"label": "generalization", "score": 0.XX}]
```
## Training Data
The model was fine-tuned on a combined denotational-relation dataset referred to as `WN+CN+UM`, constructed from existing lexical-semantic resources:
* ChainNet for metaphor, metonymy, and homonymy relations;
* UniMet for additional metonymy examples;
* WordNet for generalization, specialization, and auto-antonymy examples.
These resources use definition or synset-pair information as proxies for relations between word senses.
## Training Procedure
Definitions were concatenated and passed through `roberta-large`. A linear classification layer followed by a softmax activation was added on top to predict the relation label.
Training setup:
* maximum epochs: 10
* batch size: 8
* learning-rate schedule: linear
* warm-up: 10% of total training steps
* learning-rate search: `{1e-4, 2e-4, 1e-5, 2e-5, 1e-6}`
* model selection: best weighted F1 on the development set
* selected learning rate: `1e-6`
## Evaluation
The model was evaluated on:
1. the `WN+CN+UM` test set;
2. the SenseRel expert-annotated denotational dataset.
| Dataset | Weighted F1 |
| ----------------------------- | ----------: |
| WN+CN+UM test set | 0.734 |
| SenseRel denotational dataset | 0.684 |
On the SenseRel denotational dataset, this model achieved the best reported score among the evaluated systems.
## Citation
If you use this model, please cite:
```bibtex
@inproceedings{cassotti-etal-2026-senserel,
title = "{S}ense{R}el: A Sense-Level Benchmark for Denotational and Connotational Meaning Relations",
author = "Cassotti, Pierluigi and
Baes, Naomi and
De Pascale, Stefano and
de S{\'a}, J{\'a}der Martins Camboim and
Periti, Francesco and
Haslam, Nick and
Geeraerts, Dirk and
Tahmasebi, Nina",
booktitle = "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.20/",
pages = "499--515"
}
```
|