Abstract
Topic modeling is one of the most applied and active research areas in the domain of information retrieval. Topic modeling has become increasingly important due to the large and varied amount of data produced every second. In this paper, we try to exploit two major drawbacks (topic independence and unsupervised learning) of latent Dirichlet allocation (LDA). To remove the first drawback, we use Wikipedia as a knowledge source to make a semi-supervised model (Source-LDA) for generating predefined topic-word distribution. The second drawback is removed using a correlation matrix containing cosine-similarity measure of all the topics. The reason for using a semi-supervised LDA instead of a supervised model is not to overfit the data for new labels. Experimental results show that the performance of Source-LDA combine with correlation matrix is better than the traditional LDA and Source-LDA.
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References
D. M. Blei, A. Y. Ng, and M. I. Jordan, “Latent dirichlet allocation,” Journal of machine Learning research, vol. 3, no. Jan, pp. 993–1022, 2003.
S. Lacoste-Julien, F. Sha, and M. I. Jordan, “Disclda: Discriminative learning for dimensionality reduction and classification,” in Advances in neural information processing systems, vol. 21, 2008, pp. 897–904.
J. Zhu, A. Ahmed, and E. P. Xing, “Medlda: maximum margin supervised topic models for regression and classification,” in Proceedings of the 26th annual international conference on machine learning. ACM, 2009, pp. 1257–1264.
L. Du, L. Ren, L. Carin, and D. B. Dunson, “A bayesian model for simultaneous image clustering, annotation and object segmentation,” in Advances in neural information processing systems, 2009, pp. 486–494.
R. K. Roul, S. R. Asthana, and G. Kumar, “Study on suitability and importance of multilayer extreme learning machine for classification of text data,” Soft Computing, vol. 21, no. 15, pp. 4239–4256, 2017.
K. Toutanova and M. Johnson, “A bayesian lda-based model for semi-supervised part-of-speech tagging,” in Advances in neural information processing systems, 2008, pp. 1521–1528.
A. Mukherjee and B. Liu, “Aspect extraction through semi-supervised modeling,” in Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers-Volume 1. Association for Computational Linguistics, 2012, pp. 339–348.
D. Wang, M. Thint, and A. Al-Rubaie, “Semi-supervised latent dirichlet allocation and its application for document classification,” in Proceedings of the the 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology-Volume 03. IEEE Computer Society, 2012, pp. 306–310.
S. Bodrunova, S. Koltsov, O. Koltsova, S. Nikolenko, and A. Shimorina, “Interval semi-supervised lda: Classifying needles in a haystack,” in Mexican International Conference on Artificial Intelligence. Springer, 2013, pp. 265–274.
R. K. Roul and S. Sanjay, “Cluster labelling using chi-square-based keyword ranking and mutual information score: a hybrid approach,” International Journal of Intelligent Systems Design and Computing, vol. 1, no. 2, pp. 145–167, 2017.
S. Jameel, W. Lam, and L. Bing, “Supervised topic models with word order structure for document classification and retrieval learning,” Information Retrieval Journal, vol. 18, no. 4, pp. 283–330, 2015.
C. D. Manning, P. Raghavan, H. Schütze et al., Introduction to information retrieval. Cambridge university press Cambridge, 2008, vol. 1, no. 1.
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Roul, R.K., Sahoo, J.K. (2019). Document Labeling Using Source-LDA Combined with Correlation Matrix. In: Behera, H., Nayak, J., Naik, B., Abraham, A. (eds) Computational Intelligence in Data Mining. Advances in Intelligent Systems and Computing, vol 711. Springer, Singapore. https://doi.org/10.1007/978-981-10-8055-5_62
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DOI: https://doi.org/10.1007/978-981-10-8055-5_62
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