SVD-LDA: A Combined Model for Text Classification
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초록
Text data has always accounted for a major portion of the world’s information. As the volume of information increases exponentially, the portion of text data also increases significantly. Text classification is therefore still an important area of research. LDA is an updated, probabilistic model which has been used in many applications in many other fields. As regards text data, LDA also has many applications, which has been applied various enhancements. However, it seems that no applications take care of the input for LDA. In this paper, we suggest a way to map the input space to a reduced space, which may avoid the unreliability, ambiguity and redundancy of individual terms as descriptors. The purpose of this paper is to show that LDA can be perfectly performed in a “clean and clear” space. Experiments are conducted on 20 News Groups data sets. The results show that the proposed method can boost the classification results when the appropriate choice of rank of the reduced space is determined.
Abstract
Text data has always accounted for a major portion of the world’s information. As the volume of information increases exponentially, the portion of text data also increases significantly. Text classification is therefore still an important area of research. LDA is an updated, probabilistic model which has been used in many applications in many other fields. As regards text data, LDA also has many applications, which has been applied various enhancements. However, it seems that no applications take care of the input for LDA. In this paper, we suggest a way to map the input space to a reduced space, which may avoid the unreliability, ambiguity and redundancy of individual terms as descriptors. The purpose of this paper is to show that LDA can be perfectly performed in a “clean and clear” space. Experiments are conducted on 20 News Groups data sets. The results show that the proposed method can boost the classification results when the appropriate choice of rank of the reduced space is determined.
참고문헌 (18)
- Zhiwei Zhang, "An Efficient Feature Selection using Hidden Topics in Text Categorization", 22nd International Conference on Advanced Information Networking and Application (2008)
- A. Berger, "A maximum entropy approach to natural language processing", Computational Linguistics, 22(1), pp.39~71 (1996)
- S. Deerwester, "Indexing by latent semantic analysis", Journal of the American Society for Info, Science, 41(6), pp.391~407 (1990)
- D. M. Blei, "Latent Dirichlet Allocation", JMLR, 3, pp.993~1022 (2003)
- Ramesh Nallapati, "Link-PLSALDA:A new unsupervised model for topics and the influence of blogs", AAAI (2008)
- Parameter estimation for text analysis, "G.Heinrich", University of Leipzig, ~ (2005)
- T.Hofmann, "Probabilistic latent semantic indexing", Proceedings of SIGIR'99 (1999)
- Tuomo Kakkonen, "Applying Latent Dirichlet Allocation to Automatic Essay Grading", FinTAL 2006,LNAI, 4139, pp.110~120 (2006)
- F. Sebastiani, "Machine learning in automated text categorization", ACM Computing Surveys, 34(1), pp.2002~1 (2002), 47
- Y. Yang, "A Comparative Study on Feature Selection in Text Categorization", Proceedings of the Fourteenth International Conference on Machine Learning, pp.412~420 (1997)
- C. Andrieu, "An introduction to MCMC for machine learning", Machine Learning, 50, pp.5~43 (2003)
- T. Hofmann, "Unsupervised learning from dyadic data", Advances in Neural Information Processing Systems, 11 (1999)
- B.C. Russell, "Using Multiple Segmentations to Discover Objects and their Extent in Image Collections", Proceedings of CVPR (2006)
- T. Hofmann, "Latent semantic models for collaborative filtering", ACM TOIS, 22(1), pp.89~115 (2004)
- T. Minka, "Expectation-propagation for the generative aspect model", Proc.UAI (2002)
- F. Sebastiani, "Machine learning in automated text categorization", ACM Computing Surveys, 34(1), pp.1~47 (2002)