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Text categorization (TC) is a challenging issue, and the corresponding algorithms can be used in many applications. This paper addresses the online multi-category TC problem abstracted from the applications of online binary TC and batch multi-category TC. Most applications are concerned about the space-time performance of TC algorithms. Through the investigation of the token frequency distribution in an email collection and a Chinese web document collection, this paper re-examines the power law and proposes a random sampling ensemble Bayesian (RSEB) TC algorithm. Supported by a token level memory to store labeled documents, the RSEB algorithm uses a text retrieval approach to solve text categorization problems. The experimental results show that the RSEB algorithm can achieve the state-of-the-art performance at greatly reduced space-time requirements both in the TREC email spam filtering task and the Chinese web document classifying task.

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Liu, W., Wang, L., Yi, M. (2013). Power Law for Text Categorization. In: Sun, M., Zhang, M., Lin, D., Wang, H. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. NLP-NABD CCL 2013 2013. Lecture Notes in Computer Science(), vol 8202. Springer, Berlin, Heidelberg.

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  • Print ISBN: 978-3-642-41490-9

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