Abstract
Text categorization (TC) has become one the most researched fields in NLP. In this paper, we try to solve the problem of TC through a 2-step feature selection approach. First we cluster the words that appear in the texts according to their distribution in categories. Then we extract concepts from these clusters, which are DEF terms in HowNet. The extraction is according to the word clusters instead of single words. This method maintains the generalization ability of concept extraction based TC and at the same time makes full use of the occurrences of new words that are not found in concept thesaurus. We test the performance of our feature selection method on the Sogou corpus for TC with an SVM classifier. Results of our experiments show that our method can improve the performance of TC in all categories.
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He, Y., Jiang, M. (2007). Text Categorization Using Distributional Clustering and Concept Extraction. In: Huang, DS., Heutte, L., Loog, M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Theoretical and Methodological Issues. ICIC 2007. Lecture Notes in Computer Science, vol 4681. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74171-8_71
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DOI: https://doi.org/10.1007/978-3-540-74171-8_71
Publisher Name: Springer, Berlin, Heidelberg
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