MPE Mathematical Problems in Engineering 1563-5147 1024-123X Hindawi Publishing Corporation 347257 10.1155/2012/347257 347257 Research Article Distributional Similarity for Chinese: Exploiting Characters and Radicals Jin Peng jandp@pku.edu.cn 1 Carroll John j.a.carroll@sussex.ac.uk 2 Wu Yunfang wuyf@pku.edu.cn 3 McCarthy Diana diana@dianamccarthy.co.uk 4 Wang Yuping 1 School of Computer Science Leshan Normal University 614004 Leshan China lsnu.edu.cn 2 Department of Informatics Sussex University Brighton BN1 9QJ UK sussex.ac.uk 3 Institute of Computational Linguistics Peking University 100871 Beijing China pku.edu.cn 4 Department of Theoretical and Applied Linguistics, University of Cambridge, Cambridge CB3 9DB UK cam.ac.uk 2012 15 8 2012 2012 10 04 2012 01 06 2012 2012 Copyright © 2012 Peng Jin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Distributional 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.

http://dx.doi.org/10.13039/501100001809 National Natural Science Foundation of China 61003206 http://dx.doi.org/10.13039/501100001809 National Natural Science Foundation of China 60703063 http://dx.doi.org/10.13039/501100001809 National Natural Science Foundation of China 61103089