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---
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
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Country: country
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extra_gated_heading: >-
Acknowledge our [Creative Commons Attribution-NonCommercial-ShareAlike 4.0
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4.0)](https://github.com/Babelscape/WSL/wsl_data_license.txt) to access the
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extra_gated_description: Our team may take 2-3 days to process your request
extra_gated_button_content: Acknowledge license
---
---
# Word Sense Linking: Disambiguating Outside the Sandbox
[](https://2024.aclweb.org/)
[](https://aclanthology.org/2024.findings-acl.851/)
[](https://huggingface.co/collections/Babelscape/word-sense-linking-66ace2182bc45680964cefcb)
## Model Description
We introduce the task of Word Sense Linking (WSL), which focuses on accurately mapping spans of text to their most appropriate senses using a reference inventory. 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")
wsl_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.
## 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. |