Hierarchical Text Classification Based on LDA and Domain Ontology

Wei An; Qi Hua Liu

doi:10.4028/www.scientific.net/AMM.411-414.1112

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 411-414Hierarchical Text Classification Based on LDA and...

Hierarchical Text Classification Based on LDA and Domain Ontology

Abstract:

This paper combines domain ontology and LDA model to propose a new method of hierarchical web text classification. Experimental results show that the method has good performance with high recall rate and accuracy rate.

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Periodical:

Applied Mechanics and Materials (Volumes 411-414)

Pages:

1112-1116

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.411-414.1112

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Online since:

September 2013

Authors:

Wei An*, Qi Hua Liu

Keywords:

Domain Ontology, Hierarchical Text Classification, Latent Dirichlet Allocation (LDA), Text Classification

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References

[1] K.S. Goh, E.Y. Chang, and B. Li: IEEE Transactions on Knowledge, Vol. 17 (2005), pp.1333-1346.

Google Scholar

[2] J. Novovicova, and A. Malik. in: Proceedings of the pattern recognition and image analysis, 2003, pp.646-653.

Google Scholar

[3] T. Luo, K. Kramer, D.B. Goldgof, L.O. Hall, S. Samson, and A. Remsen: IEEE Transactions on Systems, Man and Cybernetics, Vol. 34(2004), pp.1753-1762.

DOI: 10.1109/tsmcb.2004.830340

Google Scholar

[4] X. Huang, W. Pan, S. Grindle, X. Han, Y. Chen, S.J. Park: Bioinformatics, Vol. 205(2005).

Google Scholar

[5] L. Devillers, L. Vidrascu, and L. Lamel: Neural Networks, Vol. 18(2005), pp.407-422.

Google Scholar

[6] A. Prinzie, and D. Van den Poel: Expert Systems with Applications, Vol. 29(2005), pp.630-640.

Google Scholar

[7] A. Prinzie, and D. Van den Poel: Expert Systems with Applications, Vol. 34(2008), pp.1721-1732.

DOI: 10.1016/j.eswa.2007.01.029

Google Scholar

[8] Q. Liu: Journal of Computational Information Systems, Vol. 8 (2012), p.1897-(1904).

Google Scholar

[9] D. M. Blei, A. Y. Ng, and M. I. Jordan: Journal of Machine Learning Research, Vol. 1 (2003), pp.993-1022.

Google Scholar

[10] T. L. Griffiths and M. Steyvers: Proceedings of the National Academy of Sciences of the United States of America, Vol. 100 (2004), pp.5228-5235.

Google Scholar

[11] Y. W. Teh, M. I. Jordan, M. J. Beal, and D.M. Blei: Journal of the American Statistical Association, Vol. 476(2006), pp.1566-1581.

DOI: 10.1198/016214506000000302

Google Scholar

[12] J. T. Chien and M. S. Wu: IEEE Transactions on Audio, Speech, and Language Processing, Vol. 16 (2008), pp.198-207.

Google Scholar

[13] A. Asuncion, M. Welling, P. Smyth, and Y. W. The, in: Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, 2009, pp.27-34.

Google Scholar

[14] http: /www. cnhhl. com.

Google Scholar