Approches d'analyse distributionnelle pour améliorer la désambiguïsation sémantique
  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.
