A Comparison and Semi-Quantitative Analysis of Words and Character-Bigrams as Features in Chinese Text Categorization

Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the ACL, pages 545–552, Sydney, July 2006. c⃝2006 Association for Computational Linguistics A Comparison and Semi-Quantitative Analysis of Words and Character-Bigrams as Features in Chinese Text Categorization Jingyang Li Maosong Sun Xian Zhang National Lab. of Intelligent Technology & Systems, Department of Computer Sci. & Tech. Tsinghua University, Beijing 100084, China lijingyang@gmail.com sms@tsinghua.edu.cn kevinn9@gmail.com Abstract Words and character-bigrams are both used as features in Chinese text process- ing tasks, but no systematic comparison or analysis of their values as features for Chinese text categorization has been re- ported heretofore. We carry out here a full performance comparison between them by experiments on various docu- ment collections (including a manually word-segmented corpus as a golden stan- dard), and a semi-quantitative analysis to elucidate the characteristics of their be- havior; and try to provide some prelimi- nary clue for feature term choice (in most cases, character-bigrams are better than words) and dimensionality setting in text categorization systems. 1 Introduction1 Because of the popularity of the Vector Space Model (VSM) in text information processing, document indexing (term extraction) acts as a pre-requisite step in most text information proc- essing tasks such as Information Retrieval (Baeza-Yates and Ribeiro-Neto, 1999) and Text Categorization (Sebastiani, 2002). It is empiri- cally known that the indexing scheme is a non- trivial complication to system performance, es- pecially for some Asian languages in which there are no explicit word margins and even no natural semantic unit. Concretely, in Chinese Text Cate- gorization tasks, the two most important index- 1 This research is supported by the National Natural Science Foundation of China under grant number 60573187 and 60321002, and the Tsinghua-ALVIS Project co-sponsored by the National Natural Science Foundation of China under grant number 60520130299 and EU FP6. ing units (feature terms) are word and character- bigram, so the problem is: which kind of terms2 should be chosen as the feature terms, words or character-bigrams? To obtain an all-sided idea about feature choice beforehand, we review here the possible feature variants (or, options). First, at the word level, we can do stemming, do stop-word prun- ing, include POS (Part of Speech) information, etc. Second, term combinations (such as “word- bigram”, “word + word-bigram”, “character- bigram + character-trigram”3, etc.) can also be used as features (Nie et al., 2000). But, for Chi- nese Text Categorization, the “word or bigram” question is fundamental. They have quite differ- ent characteristics (e.g. bigrams overlap each other in text, but words do not) and influence the classification performance in different ways. In Information Retrieval, it is reported that bi- gram indexing schemes outperforms word schemes to some or little extent (Luk and Kwok, 1997; Leong and Zhou 1998; Nie et al., 2000). Few similar comparative studies have been re- ported for Text Categorization (Li et al., 2003) so far in literature. Text categorization and Information Retrieval are tasks that sometimes share identical aspects (Sebastiani, 2002) apart from term extraction (document indexing), …

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