| # 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" |
| } |
| ``` |
|
|
