  We describe a method for automatic word sense disambiguation using a text
corpus and a machine-readable dictionary (MRD). The method is based on word
similarity and context similarity measures. Words are considered similar if
they appear in similar contexts; contexts are similar if they contain similar
words. The circularity of this definition is resolved by an iterative,
converging process, in which the system learns from the corpus a set of typical
usages for each of the senses of the polysemous word listed in the MRD. A new
instance of a polysemous word is assigned the sense associated with the typical
usage most similar to its context. Experiments show that this method performs
well, and can learn even from very sparse training data.
