Abstract
This paper presents an approach to the topic extraction from text documents using probabilistic graphical models. Multiple-cause networks with latent variables are used and the Helmholtz machines are utilized to ease the learning and inference. The learning in this model is conducted in a purely data-driven way and does not require prespecified categories of the given documents. Topic words extraction experiments on the TDT-2 collection are presented. Especially, document clustering results on a subset of TREC-8 ad-hoc task data show the substantial reduction of the inference time without significant deterioration of performance.
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References
Dayan, P., Hinton, G.E., Neal, R. M., Zemel, R. S.: The Helmholtz machine. Neural Computation 7 (1995) 889–904
Dayan, P., Zemel, R.S.: Competition and multiple cause models. Neural Computation 7 (1995) 565–579
Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., Harshman, R.: Indexing by latent semantic analysis. Journal of the American Society for Information Science. 41 (1990) 391–407
deSa, V.R., deCharms, R.C., Merzenich, M.M.: Using Helmholtz machines to analyze multi-channel neuronal recordings. Advances in Neural Information Processing Systems 10 (1998) 131–137
Frey, B.J.: Graphical Models for Machine Learning and Digital Communication. The MIT Press (1998)
Hinton, G.E., Dayan, P., Frey, B.J., Neal, R.M.: The wake-sleep algorithm for unsupervised neural networks. Science 268 (1995) 1158–1161.
Hofmann, T.: Probabilistic latent semantic indexing. Proceedings of the 22th International Conference on Research and Development in Information Retrieval (SIGIR) (1999) 50–57
Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. Nature 401 (1999) 788–791
Sahami, M., Hearst, M., Saund, E.: Applying the multiple cause mixture model to Text Categorization. Proceedings of the 13th International Conference on Machine Learning (1996) 435–443
Saund, E.: A multiple cause mixture model for unsupervised learning. Neural Computation 7 (1995) 51–71
Teh, Y.W., Hinton, G.E.: Rate-coded restricted Boltzmann machines for face recognition. Advances in Neural Information Processing Systems 13 (2001) 908–914
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© 2002 Springer-Verlag Berlin Heidelberg
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Chang, JH., Won Lee, J., Kim, Y., Zhang, BT. (2002). Topic Extraction from Text Documents Using Multiple-Cause Networks. In: Ishizuka, M., Sattar, A. (eds) PRICAI 2002: Trends in Artificial Intelligence. PRICAI 2002. Lecture Notes in Computer Science(), vol 2417. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45683-X_47
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DOI: https://doi.org/10.1007/3-540-45683-X_47
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