Approches d'analyse distributionnelle pour am\'eliorer la d\'esambigu\'isation s\'emantique
| Autor: | Billami, Mokhtar, Gala, Núria |
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| Jazyk: | francouzština |
| Rok vydání: | 2017 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | Word sense disambiguation (WSD) improves many Natural Language Processing (NLP) applications such as Information Retrieval, Machine Translation or Lexical Simplification. WSD is the ability of determining a word sense among different ones within a polysemic lexical unit taking into account the context. The most straightforward approach uses a semantic proximity measure between the word sense candidates of the target word and those of its context. Such a method very easily entails a combinatorial explosion. In this paper, we propose two methods based on distributional analysis which enable to reduce the exponential complexity without losing the coherence. We present a comparison between the selection of distributional neighbors and the linearly nearest neighbors. The figures obtained show that selecting distributional neighbors leads to better results. Comment: in French, Journ\'ees internationales d'Analyse statistique des Donn\'ees Textuelles (JADT), Jun 2016, Nice, France |
| Databáze: | arXiv |
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