Target word selection as proximity in semantic space

Authors:
Scott McDonald
Affiliations:
University of Edinburgh, Edinburgh, Scotland
Venue:
ACL '98 Proceedings of the 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics - Volume 2
Year:
1998

Citing 3
Cited 0

Dimensions of meaning

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Word sense disambiguation using a second language monolingual corpus

Computational Linguistics
Choosing the word most typical in context using a lexical co-occurrence network

ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics

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Abstract

Lexical selection is a significant problem for wide-coverage machine translation: depending on the context, a given source language word can often be translated into different target language words. In this paper I propose a method for target word selection that assumes the appropriate translation is more similar to the translated context than are the alternatives. Similarity of a word to a context is estimated using a proximity measure in corpus-derived "semantic space". The method is evaluated using an English-Spanish parallel corpus of colloquial dialogue.