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