| # Denotational Type Classifier |
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| 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. |
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| The model predicts one of the following relation types: |
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| * `generalization` |
| * `specialization` |
| * `metaphor` |
| * `metonymy` |
| * `homonymy` |
| * `antonymy` |
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| 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. |
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| ## Model Details |
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| * **Model type:** Causal language model |
| * **Base model:** `meta-llama/Meta-Llama-3.1-8B` |
| * **Task:** Denotational relation classification via text generation |
| * **Language:** English |
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| ## Intended Use |
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| 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. |
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| Example use cases include: |
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| * studying semantic change in dictionaries or lexical resources; |
| * analyzing polysemous word senses; |
| * supporting sense-level semantic annotation; |
| * scaling exploratory studies of denotational change. |
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| The model is not intended for high-stakes decision-making or for general-purpose natural language understanding outside lexical-semantic relation classification. |
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| ## Input Format |
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| ```python |
| from transformers import AutoTokenizer, AutoModelForCausalLM |
| import torch |
| |
| model_id = "ChangeIsKey/denotational-llama-classifier" |
| |
| tokenizer = AutoTokenizer.from_pretrained(model_id) |
| model = AutoModelForCausalLM.from_pretrained(model_id) |
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| definition_1 = "the young of the domestic cow" |
| definition_2 = "the young of various large mammals" |
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| text = f"{definition_1} <|s|> {definition_2} <|t|>" |
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| inputs = tokenizer(text, return_tensors="pt") |
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| with torch.no_grad(): |
| outputs = model(**inputs) |
| next_token_logits = outputs.logits[0, -1, :] |
| |
| label_tokens = [ |
| "<|generalization|>", |
| "<|specialization|>", |
| "<|metaphor|>", |
| "<|metonymy|>", |
| "<|homonymy|>", |
| "<|antonymy|>", |
| ] |
| |
| label_token_ids = tokenizer.convert_tokens_to_ids(label_tokens) |
| label_logits = next_token_logits[label_token_ids] |
| predicted_id = label_token_ids[torch.argmax(label_logits).item()] |
| |
| prediction = tokenizer.convert_ids_to_tokens(predicted_id) |
| print(prediction) |
| ``` |
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| ## Training Data |
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| The model was fine-tuned on a combined denotational-relation dataset referred to as `WN+CN+UM`, constructed from existing lexical-semantic resources: |
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| * ChainNet for metaphor, metonymy, and homonymy relations; |
| * UniMet for additional metonymy examples; |
| * WordNet for generalization, specialization, and auto-antonymy examples. |
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| These resources use definition or synset-pair information as proxies for relations between word senses. |
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| ## Training Procedure |
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| The base Llama model was fine-tuned using a causal language modeling objective. |
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| Each training instance consists of a prompt containing two dictionary definitions followed by the target relation label. During fine-tuning, the model learns to generate the correct label given the input definitions. |
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| ## Evaluation |
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| The model was evaluated on: |
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| 1. the `WN+CN+UM` test set; |
| 2. the SenseRel expert-annotated denotational dataset. |
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| | Dataset | Weighted F1 | |
| | ----------------------------- | ----------: | |
| | WN+CN+UM test set | 0.737 | |
| | SenseRel denotational dataset | 0.658 | |
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| On the WN+CN+UM test set, this model achieved the best reported score among the evaluated systems. |
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| ## Citation |
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| If you use this model, please cite: |
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| ```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" |
| } |
| ``` |
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