{
 "id": "GETALP/FLUE_VSD",
 "owner": "GETALP",
 "name": "FLUE_VSD",
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 "last_modified": "2023-04-19T15:11:11.000Z",
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 "citation": "@inproceedings{segonne-etal-2019-using,\n    title = \"Using {W}iktionary as a resource for {WSD} : the case of {F}rench verbs\",\n    author = \"Segonne, Vincent  and\n      Candito, Marie  and\n      Crabb{\\'e}, Beno{\\^\\i}t\",\n    booktitle = \"Proceedings of the 13th International Conference on Computational Semantics - Long Papers\",\n    month = may,\n    year = \"2019\",\n    address = \"Gothenburg, Sweden\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/W19-0422\",\n    doi = \"10.18653/v1/W19-0422\",\n    pages = \"259--270\",\n    abstract = \"As opposed to word sense induction, word sense disambiguation (WSD) has the advantage of us-ing interpretable senses, but requires annotated data, which are quite rare for most languages except English (Miller et al. 1993; Fellbaum, 1998). In this paper, we investigate which strategy to adopt to achieve WSD for languages lacking data that was annotated specifically for the task, focusing on the particular case of verb disambiguation in French. We first study the usability of Eurosense (Bovi et al. 2017) , a multilingual corpus extracted from Europarl (Kohen, 2005) and automatically annotated with BabelNet (Navigli and Ponzetto, 2010) senses. Such a resource opened up the way to supervised and semi-supervised WSD for resourceless languages like French. While this perspective looked promising, our evaluation on French verbs was inconclusive and showed the annotated senses{'} quality was not sufficient for",
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