C. K. P. Wong
R. W. P. Luk
ABSTRACT
This paper evaluated text categorization using charactes, bigrams, words and hybrid terms. These terms were also augmented with mined terms. Classifiers using hybrid terms did not achieve better classification performance. The use of data mining techniques to add new terms to the dictionary improves the performance of character-based classifiers. Our naïve comparison between the Pat-tree classifier and our best classifier shows that the Pat-tree classifier has the best precision (77%) and our best classifier has the best recall (72%) and the lowest storage requirement (13%).
- 1.Lewis, D.D. (1992) "An evaluation of phrasal and clustered representations on a text categorization task", Proc. of 15th ACM SIGIR, pp.37--50. Google Scholar
Digital Library
- 2.Chen, C.L. and L.-F. Chien (1999) "PAT-tree based online corpus classification with an application to OCR text verification", 1RAL Workshop 1999.Google Scholar
- 3.Lam, W., C-Y Wong and K.F. Wong (1997) Performance Evaluation of Character-, Word- and N- Gram-Based Indexing for Chinese Text Retrieval, IRAL 97, Japan.Google Scholar
- 4.Tsang, T.F., R.W.P. Luk and K.F. Wong (1999) A Hybrid terms indexing strategy using words and bigrams, IRAL 99, Taiwan.Google Scholar
- 5.Van Rijsbergen, C.V. (1979) Information Retrieval, Butterworths, London. Google ScholarDigital Library
- 6.Lin, Y.H. and A.K. Jain (1998) Classification of text documents, The Computer Journal, 41(8), 537--546.Google ScholarCross Ref
- 7.Fung, P. and D. Wu (1994) Statistical Augmentation of a Chinese Machine-readable dictionary, Proceedings of Workshop on Very Large Corpora, Kyoto, August.Google Scholar
- Text categorization using hybrid (mined) terms (poster session)
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