Abstract

We present an unsupervised method for word sense disambiguation that exploits translation correspondences in parallel corpora. The technique takes advantage of the fact that crosslanguage lexicalizations of the same concept tend to be consistent, preserving some core element of its semantics, and yet also variable, reflecting differing translator preferences and the influence of context. Working with parallel corpora introduces an extra complication for evaluation, since it is difficult to find a corpus that is both sense tagged and parallel with another language; therefore we use pseudotranslations, created by machine translation systems, in order to make possible the evaluation of the approach against a standard test set. The results demonstrate that word-level transla- tion correspondences are a valuable source of information for sense disam- biguation.

Citations