  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.
