Journal of Guangxi Normal University(Natural Science Edition) ›› 2010, Vol. 28 ›› Issue (1): 157-160.

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Cross-language Text Classification Based on Latent Semantic DualSpace

XIONG Chao1, WANG Ming-wen1, WU Fu-ying2, WU Shi-yong1, SHEN Yang2   

  1. 1. School of Computer Information Engineering,Jiangxi Normal University,Nanchang Jiangxi 330022,China;
    2. School of Software,Jiangxi Normal University,Nanchang Jiangxi 330022,China;
    3. Jiangxi Microsoft Technology Center,Nanchang Jiangxi 330096,China
  • Received:2009-12-20 Online:2010-03-20 Published:2023-02-07

Abstract: Nowadays,with the trend of language diversity inthe internet,how to organize multi-language resources becomes a hotspot.This paper focuses on cross language text categorization (CLTC) which can organize heterogeneous document collections.Using the semantics pairs extracted from parallel.The latent semantic dual space can be built by using the semantics pairs extracted from parallel corpus.In the experiment,through changing the trainingsize and language composition the performance of CLTC can be verified.Results show that cross-language text classification based on latent semantic dual space makes a good performance in stability and accuracy.

Key words: CLTC, latent semantic dual space, semantic pairs, parallel corpus

CLC Number: 

  • TP391.1
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