Neural Text Categorizer for Exclusive Text Categorization
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초록
This research proposes a new neural network for text categorization which uses alternative representations of documents to numerical vectors. Since the proposed neural network is intended originally only for text categorization, it is called NTC (Neural Text Categorizer) in this research. Numerical vectors representing documents for tasks of text mining have inherently two main problems: huge dimensionality and sparse distribution. Although many various feature selection methods are developed to address the first problem, the reduced dimension remains still large. If the dimension is reduced excessively by a feature selection method, robustness of text categorization is degraded. Even if SVM (Support Vector Machine) is tolerable to huge dimensionality, it is not so to the second problem. The goal of this research is to address the two problems at same time by proposing a new representation of documents and a new neural network using the representation for its input vector.
Abstract
This research proposes a new neural network for text categorization which uses alternative representations of documents to numerical vectors. Since the proposed neural network is intended originally only for text categorization, it is called NTC (Neural Text Categorizer) in this research. Numerical vectors representing documents for tasks of text mining have inherently two main problems: huge dimensionality and sparse distribution. Although many various feature selection methods are developed to address the first problem, the reduced dimension remains still large. If the dimension is reduced excessively by a feature selection method, robustness of text categorization is degraded. Even if SVM (Support Vector Machine) is tolerable to huge dimensionality, it is not so to the second problem. The goal of this research is to address the two problems at same time by proposing a new representation of documents and a new neural network using the representation for its input vector.
참고문헌 (21)
- Androutsopoulos, K. Koutsias, "An Experimental Comparison of Naïve Bayes and Keyword-based Anti-spam Filtering with personal email message", The Proceedings of 23rd ACM SIGIR, pp.160~167 (2000)
- N. Cristianini, "Support Vector Machines and Other Kernel-based Learning Methods" (2000), Cambridge University Press
- H. Drucker, "Support Vector Machines for Spam Categorization", IEEE Transaction on Neural Networks, 10(5), pp.1048~1054 (1999)
- R. O. Duda, "Pattern Classification" (2001), John Wiley & Sons, Inc.
- V. I. Frants, "Automated Information Retrieval: Theory and Methods" (1997), Academic Press
- M. T. Hagan, "Neural Network Design" (1995), PWS Publishing Company
- S. Haykin, "Neural Networks: Comprehensive Foundation" (1994), Macmillan College Publishing Company
- M. Hearst, "Support Vector Machines", IEEE Intelligent Systems, 13(4), pp.18~28 (1998)
- P. Jackson, "Natural Language Processing for Online Applications: Text Retrieval, Extraction and Categorization" (2002), John Benjamins Publishing Company
- T. Joachims, "Text Categorization with Support Vector Machines : Learning with many Relevant Features", The Proceedings of 10th European Conference on Machine Learning, pp.143~151 (1998)
- H. Lodhi, "Text Classification with String Kernels", Journal of Machine Learning Research, 2(2), pp.419~444 (2002)
- T. Martin, "Neural Network Design" (1995), PWS Publishing Company
- B. Massand, "Classifying News Stories using Memory based Reasoning", The Proceedings of 15th ACM International Conference on Research and Development in Information Retrieval, pp.59~65 (1992)
- T. M. Mitchell, "Machine Learning" (1997), McGraw-Hill
- D. Mladenic, "Feature Selection for unbalanced class distribution and Naïve Bayes", The Proceedings of International Conference on Machine Learning, pp.256~267 (1999)
- Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines, "J. C. Platt", ~, pp.Technical Report MSR-TR-98-14 (1998)
- J. Rennie, "Improving multi-class text classification with support vector machine", 석사, pp.2001
- M.E. Ruiz, "Hierarchical Text Categorization Using Neural Networks", Information Retrieval, 5(1), pp.87~118 (2002)
- F. Sebastiani, "Machine Learning in Automated Text Categorization", ACM Computing Survey, 34(1), pp.1~47 (2002)
- E. D. Wiener, "Neural Network Approach to Topic Spotting in Text", 석사, pp.1995
- Y. Yang, "An evaluation of statistical approaches to text categorization", Information Retrieval, 1(1), pp.67~88 (1999)