Word association norms, mutual information, and lexicography
Computational Linguistics
Two languages are more informative than one
ACL '91 Proceedings of the 29th annual meeting on Association for Computational Linguistics
An unsupervised method for word sense tagging using parallel corpora
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Unsupervised sense disambiguation using bilingual probabilistic models
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
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Lexical disambiguation can be achieved using different sources of information. Aiming at high performance of automatic disambiguation it is important to know the relative importance and applicability of the various sources. In this paper we classify several sources of information and show how some of them can be achieved using statistical data. First evaluations indicate the extreme importance of local information, which mainly represents lexical associations and selectional restrictions for syntactically related words.