TitleDistributional Similarity for Chinese: Exploiting Characters and Radicals
AuthorsJin, Peng
Carroll, John
Wu, Yunfang
McCarthy, Diana
AffiliationLeshan Normal Univ, Sch Comp Sci, Leshan 614004, Peoples R China.
Univ Sussex, Dept Informat, Brighton BN1 9QJ, E Sussex, England.
Peking Univ, Inst Computat Linguist, Beijing 100871, Peoples R China.
Univ Cambridge, Dept Theoret & Appl Linguist, Cambridge CB3 9DB, England.
Issue Date2012
Publishermathematical problems in engineering
AbstractDistributional Similarity has attracted considerable attention in the field of natural language processing as an automatic means of countering the ubiquitous problem of sparse data. As a logographic language, Chinese words consist of characters and each of them is composed of one or more radicals. The meanings of characters are usually highly related to the words which contain them. Likewise, radicals often make a predictable contribution to the meaning of a character: characters that have the same components tend to have similar or related meanings. In this paper, we utilize these properties of the Chinese language to improve Chinese word similarity computation. Given a content word, we first extract similar words based on a large corpus and a similarity score for ranking. This rank is then adjusted according to the characters and components shared between the similar word and the target word. Experiments on two gold standard datasets show that the adjusted rank is superior and closer to human judgments than the original rank. In addition to quantitative evaluation, we examine the reasons behind errors drawing on linguistic phenomena for our explanations.
Appears in Collections:计算语言学教育部重点实验室

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