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Distributional similarity for Chinese: exploiting characters and radicals
journal contribution
posted on 2023-06-08, 16:47 authored by Peng Jin, John Carroll, Yunfang Wu, Diana Frances McCarthyDistributional 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.
History
Publication status
- Published
Journal
Mathematical Problems in EngineeringISSN
1024-123XPublisher
HindawiExternal DOI
Issue
347257Volume
2012Page range
1-11Department affiliated with
- Informatics Publications
Full text available
- No
Peer reviewed?
- Yes