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arXiv · 1702.08451

Approches d'analyse distributionnelle pour am\'eliorer la d\'esambigu\"isation s\'emantique

Mokhtar Billami·Núria Gala
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Abstract

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.

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Mokhtar Billami, Núria Gala. 2017-02-27. Approches d'analyse distributionnelle pour am\'eliorer la d\'esambigu\"isation s\'emantique. https://arxiv.org/abs/1702.08451

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