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---
license: lgpl
multilinguality:
- monolingual
language:
- fr
task_categories:
- other
task_ids:
- word-sense-disambiguation
dataset_info:
features:
- name: document_id
dtype: string
- name: sentence
dtype: string
- name: sentence_label
dtype: string
- name: sentence_first_label
dtype: string
- name: surface_forms
sequence: string
- name: labels
sequence: string
- name: first_labels
sequence: string
- name: word_id
sequence: string
- name: scores
sequence: string
- name: lemmas
sequence: string
- name: pos
sequence: string
splits:
- name: SemCor
num_bytes: 71632913
num_examples: 37176
- name: SemEval
num_bytes: 749832
num_examples: 306
- name: WNGT
num_bytes: 206691837
num_examples: 117659
download_size: 41831981
dataset_size: 279074582
---
# Word Sense Disambiguation for FLUE
## Dataset Description
- **Homepage:**
- **Repository:**
- **https://arxiv.org/pdf/1905.05677.pdf**
- **Leaderboard:**
- **loic.vial@univ-grenoble-alpes.fr**
### Dataset Summary
This dataset is splitted in 3 sub-datasets: FrenchSemEval-Task12, French WNGT and an automatic translation of SemCor.
### Supported Tasks and Leaderboards
Word Sense Disambiguation for French.
### Language
French
### Licensing Information
```
GNU Lesser General Public License
```
### Citation Information
```bibtex
@inproceedings{vial-etal-2019-sense,
title = "Sense Vocabulary Compression through the Semantic Knowledge of {W}ord{N}et for Neural Word Sense Disambiguation",
author = {Vial, Lo{\"\i}c and
Lecouteux, Benjamin and
Schwab, Didier},
booktitle = "Proceedings of the 10th Global Wordnet Conference",
month = jul,
year = "2019",
address = "Wroclaw, Poland",
publisher = "Global Wordnet Association",
url = "https://aclanthology.org/2019.gwc-1.14",
pages = "108--117",
abstract = "In this article, we tackle the issue of the limited quantity of manually sense annotated corpora for the task of word sense disambiguation, by exploiting the semantic relationships between senses such as synonymy, hypernymy and hyponymy, in order to compress the sense vocabulary of Princeton WordNet, and thus reduce the number of different sense tags that must be observed to disambiguate all words of the lexical database. We propose two different methods that greatly reduce the size of neural WSD models, with the benefit of improving their coverage without additional training data, and without impacting their precision. In addition to our methods, we present a WSD system which relies on pre-trained BERT word vectors in order to achieve results that significantly outperforms the state of the art on all WSD evaluation tasks.",
}
```
### Contributions
* loic.vial@univ-grenoble-alpes.fr
* benjamin.lecouteux@univ-grenoble-alpes.fr
* didier.schwab@univ-grenoble-alpes.fr |