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 from PyPI:
git clone https://github.com/Babelscape/WSL
cd WSL
pip install -r requirements.txt
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.
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": {}}
]
),
)
Here you can find the performances of our model on the WSL evaluation dataset.
| 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 |
| 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 |
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). Copyright of the dataset contents belongs to Babelscape. Arxiv Paper: Word Sense Linking: Disambiguating Outside the Sandbox
@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, @edobobo, @poccio and @navigli for adding this model.