TEXT CLASSIFICATION TOWARD A SCIENTIFIC FORUM

Text mining, also known as discovering knowledge from the text, which has emerged as a possible solution for the current information explosion, refers to the process of extracting non-trivial and useful patterns from unstructured text. Among the general tasks of text mining such as text clustering,summarization, etc, text classification is a subtask of intelligent information processing, which employs unsupervised learning to construct a classifier from training text by which to predict the class of unlabeled text. Because of its simplicity and objectivity in performance evaluation, text classification was usually used as a standard tool to determine the advantage or weakness of a text processing method, such as text representation, text feature selection, etc. In this paper, text classification is carried out to classify the Web documents collected from XSSC Website (http://www. xssc.ac.cn). The performance of support vector machine (SVM) and back propagation neural network (BPNN) is compared on this task. Specifically, binary text classification and multi-class text classification were conducted on the XSSC documents. Moreover, the classification results of both methods are combined to improve the accuracy of classification. An experiment is conducted to show that BPNN can compete with SVM in binary text classification; but for multi-class text classification, SVM performs much better. Furthermore, the classification is improved in both binary and multi-class with the combined method.

作 者： Wen ZHANG Xijin TANG Taketoshi YOSHIDA   作者單位： Wen ZHANG,Taketoshi YOSHIDA(School of Knowledge Science, Japan Advanced Institute of Science and Technology,1-1 Ashahidai, Tatsunokuchi, Ishikawa 923-1292, Japan)

Xijin TANG(Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100080, P.R. China) 

刊 名： 系統(tǒng)科學與系統(tǒng)工程學報（英文版）  英文刊名： JOURNAL OF SYSTEMS SCIENCE AND SYSTEMS ENGINEERING  年，卷(期)： 2007 16(3)  分類號： N94  關鍵詞： Text classification   SVM   BPNN   Xiangshan Science Conference  

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