README.md

---
license:
- cc-by-nc-sa-4.0
source_datasets:
- original
task_ids:
- word-sense-disambiguation
pretty_name: word-sense-linking-dataset
tags:
- word-sense-linking
- word-sense-disambiguation
- lexical-semantics
size_categories:
- 10K<n<100K
extra_gated_fields:
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extra_gated_heading: >-
  Acknowledge our [Creative Commons Attribution-NonCommercial-ShareAlike 4.0
  International License (CC BY-NC-SA
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extra_gated_description: Our team may take 2-3 days to process your request
extra_gated_button_content: Acknowledge license
language:
- en
base_model: microsoft/deberta-v3-base
---
---


# Word Sense Linking: Disambiguating Outside the Sandbox

[![Conference](http://img.shields.io/badge/ACL-2024-4b44ce.svg)](https://2024.aclweb.org/)
[![Paper](http://img.shields.io/badge/paper-ACL--anthology-B31B1B.svg)](https://aclanthology.org/2024.findings-acl.851/)
[![Hugging Face Collection](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-FCD21D)](https://huggingface.co/collections/Babelscape/word-sense-linking-66ace2182bc45680964cefcb)
[![GitHub](https://img.shields.io/badge/GitHub-grey?logo=github&link=https%3A%2F%2Fgithub.com%2FBabelscape%2FWSL)](https://github.com/Babelscape/WSL)

## Model Description

The Word Sense Linking model is designed to identify and disambiguate spans of text to their most suitable senses from a reference inventory. The annotations are provided as sense keys from WordNet, a large lexical database of English.

## Installation

Installation from PyPI:

```bash
git clone https://github.com/Babelscape/WSL
cd WSL
pip install -r requirements.txt
```



## Usage

WSL is composed of two main components: a retriever and a reader.
The retriever is responsible for retrieving relevant senses from a senses inventory (e.g WordNet),
while the reader is responsible for extracting spans from the input text and link them to the retrieved documents.
WSL can be used with the `from_pretrained` method to load a pre-trained pipeline.

```python
from wsl import WSL
from wsl.inference.data.objects import WSLOutput

wsl_model = WSL.from_pretrained("Babelscape/wsl-base")
relik_out: WSLOutput = wsl_model("Bus drivers drive busses for a living.")
```

    WSLOutput(
    text='Bus drivers drive busses for a living.',
    tokens=['Bus', 'drivers', 'drive', 'busses', 'for', 'a', 'living', '.'],
    id=0,
    spans=[
        Span(start=0, end=11, label='bus driver: someone who drives a bus', text='Bus drivers'),
        Span(start=12, end=17, label='drive: operate or control a vehicle', text='drive'),
        Span(start=18, end=24, label='bus: a vehicle carrying many passengers; used for public transport', text='busses'),
        Span(start=31, end=37, label='living: the financial means whereby one lives', text='living')
    ],
    candidates=Candidates(
        candidates=[
                    {"text": "bus driver: someone who drives a bus", "id": "bus_driver%1:18:00::", "metadata": {}},
                    {"text": "driver: the operator of a motor vehicle", "id": "driver%1:18:00::", "metadata": {}},
                    {"text": "driver: someone who drives animals that pull a vehicle", "id": "driver%1:18:02::", "metadata": {}},
                    {"text": "bus: a vehicle carrying many passengers; used for public transport", "id": "bus%1:06:00::", "metadata": {}},
                    {"text": "living: the financial means whereby one lives", "id": "living%1:26:00::", "metadata": {}}
        ]
    ),
)



## Model Performance

Here you can find the performances of our model on the [WSL evaluation dataset](https://huggingface.co/datasets/Babelscape/wsl).

### Validation (SE07)

| Models       | P    | R      | F1     |
|--------------|------|--------|--------|
| BEM_SUP      | 67.6 | 40.9   | 51.0   |
| BEM_HEU      | 70.8 | 51.2   | 59.4   |
| ConSeC_SUP   | 76.4 | 46.5   | 57.8   |
| ConSeC_HEU   | **76.7** | 55.4   | 64.3   |
| **Our Model**| 73.8 | **74.9** | **74.4** |

### Test (ALL_FULL)

| Models       | P    | R      | F1     |
|--------------|------|--------|--------|
| BEM_SUP      | 74.8 | 50.7   | 60.4   |
| BEM_HEU      | 76.6 | 61.2   | 68.0   |
| ConSeC_SUP   | 78.9 | 53.1   | 63.5   |
| ConSeC_HEU   | **80.4** | 64.3   | 71.5   |
| **Our Model**| 75.2 | **76.7** | **75.9** |



## Additional Information
**Licensing Information**: Contents of this repository are restricted to only non-commercial research purposes under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/). Copyright of the dataset contents belongs to Babelscape.
**Arxiv Paper:** [Word Sense Linking: Disambiguating Outside the Sandbox](https://arxiv.org/abs/2412.09370)

## Citation Information


```bibtex
@inproceedings{bejgu-etal-2024-wsl,
    title     = "Word Sense Linking: Disambiguating Outside the Sandbox",
    author    = "Bejgu, Andrei Stefan and Barba, Edoardo and Procopio, Luigi and Fern{\'a}ndez-Castro, Alberte and Navigli, Roberto",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2024",
    month     = aug,
    year      = "2024",
    address   = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
}
```
 
**Contributions**: Thanks to [@andreim14](https://github.com/andreim14), [@edobobo](https://github.com/edobobo), [@poccio](https://github.com/poccio) and [@navigli](https://github.com/navigli) for adding this model.