NDLI: A Comparative Study on Supervised and Unsupervised Learning Approaches for Multilingual Text Categorization

Content Provider	IEEE Xplore Digital Library
Author	Chung-Hong Lee Hsin-Chang Yang Ting-Chung Chen Sheng-Min Ma
Copyright Year	2006
Description	Author affiliation: Dept. of Electr. Eng., Nat. Kaohsiung Univ. of Appl. Sci. (Chung-Hong Lee)
Abstract	Recently users of internationally distributed information networks need tools and methods that enable them to discover, retrieve and categorize relevant information, in whatever language and form it may have been stored. This drives a convergence of numerous interests from diverse research communities focusing on the issues related to multilingual text categorization. In this work we compare and evaluate the performance of the leading supervised and unsupervised approaches for multilingual text categorization by using various performance measures and standard document corpora. For simplicity, we selected support vector machines (SVM) and latent semantic indexing (LSI) techniques as representatives of supervised and unsupervised methods for multilingual text categorization, respectively. The preliminary results show that our platform models including both supervised and unsupervised learning methods have the potentials for multilingual text categorization
Sponsorship	IEEE ICIC Int. National Natural Sci. Found. of China Beijing Jiaotong Univ. Kaosiung Univ. of Appl. Sci
Starting Page	511
Ending Page	514
File Size	139488
Page Count	4
File Format	PDF
ISBN	0769526160
DOI	10.1109/ICICIC.2006.189
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-08-30
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Measurement standards Text categorization Natural languages Humans Information retrieval Large scale integration Unsupervised learning Indexing Convergence
Content Type	Text
Resource Type	Article

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