Combining a Chinese Thesaurus with a Chinese Dictionary

Combining a Chinese Thesaurus with a Chinese Dictionary Ji Donghong Kent Ridge Digital Labs 21 Heng Mui Keng Terrace Singapore, 119613 dhji@krdl.org.sg Gong Junping Department of Computer Science Ohio State University Columbus, OH jgong@cis.ohio-state.edu Huang Changning Department of Computer Science Tsinghua University Beijing, 100084, P. R. China hcn @mail.tsinghua.edu.cn Abstract In this paper, we study the problem of combining a Chinese thesaurus with a Chinese dictionary by linking the word entries in the thesaurus with the word senses in the dictionary, and propose a similar word strategy to solve the problem. The method is based on the definitions given in the dictionary, but without any syntactic parsing or sense disambiguation on them at all. As a result, their combination makes the thesaurus specify the similarity between senses which accounts for the similarity between words, produces a kind of semantic classification of the senses defined in the dictionary, and provides reliable information about the lexical items on which the resources don't conform with each other. 1, Introduction Both ((TongYiCi CiLin)) (Mei. et al, 1983) and ((XianDai HanYu CiDian)) (1978) are important Chinese resources, and have been widely used in various Chinese processing systems (e.g., Zhang et al, 1995). As a thesaurus, ((TongYiCi CiLin)) defines semantic categories for words, however, it doesn't specify which sense of a polysemous word is involved in a semantic category. On the other hand, ((XianDai HanYu CiDian)) is an ordinary dictionary which provides definitions of senses while not giving any information about their semantic classification. A manual effort has been made to build a resource for English, i.e., WordNet, which contains both definition and classification information (Miller et al., 1990), but such resources are not available for many other languages, e.g. Chinese. This paper presents an automatic method to combine the Chinese thesaurus with the Chinese dictionary into such a resource, by tagging the entries in the thesaurus with appropriate senses in the dictionary, meanwhile assigning appropriate semantic codes, which stand for semantic categories in the thesaurus, to the senses in the dictionary. D.Yarowsky has considered a similar problem to link Roget's categories, an English thesaurus, with the senses in COBUILD, an English dictionary (Yarowsky, 1992). He treats the problem as a sense disambiguation one, with the definitions in the dictionary taken as a kind of contexts in which the headwords occur, and deals with it based on a statistical model of Roget's categories trained on large corpus. In our opinion, the method, for a specific word, neglects the difference between its definitions and the ordinary contexts: definitions generally contain its synonyms, hyponyms or hypernyms, etc., while ordinary contexts generally its collocations. So the trained model on ordinary contexts may be not appropriate for the disambiguation problem in definition contexts. A seemingly reasonable method to the problem would be common word strategy, which has been extensively studied by many researchers (e.g., Knight, 1993; Lesk, 1986). The solution 600 would be, for a category, to select those senses whose definitions hold most number of common words among all …

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