Construction of a Chinese-English Verb Lexicon for Embedded Machine Translation in Cross-Language Information Retrieval

Abstract

This paper addresses the problem of automatic acquisition of lexical knowledge for rapid construction of MT engines %DL: delete for use in multilingual applications. We describe new techniques for large-scale construction of a Chinese-English verb lexicon and we evaluate the coverage and effectiveness of the resulting lexicon for a structured MT approach that is embedded in a cross-language information retrieval system. Leveraging off an existing Chinese conceptual database called HowNet and a large, semantically rich English verb database, we use thematic-role information to create links between Chinese concepts and English classes. We apply the metrics of recall and precision to evaluate the coverage and effectiveness of the linguistic resources. The results of this work indicate that: (1) we are able to obtain reliable Chinese-English entries both with and without pre-existing semantic links between the two languages; (2) if we have pre-existing semantic links, we are able to produce a more robust lexical resource by merging these with our semantically rich English database; (3) In our comparisons with manual lexicon creation, our automatic techniques were shown to achieve 62% precision, compared to a much lower precision of 10% for arbitrary assignment of semantic links. (Also LAMP-TR-093) (Also UMIACS-TR-2002-80)