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Datasets:
GETALP
/
FLUE_WSD

Tasks:
Other
Sub-tasks:
word-sense-disambiguation
Languages:
French
Multilinguality:
monolingual
ArXiv:
Tags:
License:
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metadata
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

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 

@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.",
}

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