Word-for-word glossing with contextually similar words

Authors:
Patrick Pantel;Dekang Lin
Affiliations:
University of Manitoba, Winnipeg, Manitoba, Canada;University of Manitoba, Winnipeg, Manitoba, Canada
Venue:
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Year:
2000

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Abstract

Many corpus-based machine translation systems require parallel corpora. In this paper, we present a word-for-word glossing algorithm that requires only a source language corpus. To gloss a word, we first identify its similar words that occurred in the same context in a large corpus. We then determine the gloss by maximizing the similarity between the set of contextually similar words and the different translations of the word in a bilingual thesaurus.