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Soft Computing

, Volume 19, Issue 1, pp 29–38 | Cite as

Open-categorical text classification based on multi-LDA models

  • Ruiji Fu
  • Bing Qin
  • Ting LiuEmail author
Focus

Abstract

We present a new and realistic problem, open-categorical text classification, which requires us to classify documents without the categorization system known beforehand. To solve this problem, we propose a novel approach to construct the categorization system and classify documents based on multi-latent Dirichlet allocation (LDA) models. We cluster topics and extract topical keywords to help category annotation. Subsequently, the LDA models are applied to predict the categories of documents comprehensively. Our result, a macro-averaged F1 measure of 84.02 %, outperforms the state-of-the-art supervised and semi-supervised text classification methods.

Keywords

Topic model Text classification  Categorization system construction 

Notes

Acknowledgments

This work is supported by National Natural Science Foundation of China (NSFC) via Grant 61133012, 61273321 and the National 863 Leading Technology Research Project via grant 2012AA011102. Special thanks to Jianfei Guo and Xiaocheng Feng for their help in the experiments..

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Harbin Institute of TechnologyHarbinPeople’s Republic of China

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