# 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:** Causal language model
* **Base model:** `meta-llama/Meta-Llama-3.1-8B`
* **Task:** Denotational relation classification via text generation
* **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

```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)

definition_1 = "the young of the domestic cow"
definition_2 = "the young of various large mammals"

text = f"{definition_1} <|s|> {definition_2} <|t|>"

inputs = tokenizer(text, return_tensors="pt")

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)
```


## 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

The base Llama model was fine-tuned using a causal language modeling objective.

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.

## 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             |       0.737 |
| SenseRel denotational dataset |       0.658 |

On the WN+CN+UM test set, 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"
}
```