Abstract
Text categorization and retrieval tasks are often based on a good representation of textual data. Departing from the classical vector space model, several probabilistic models have been proposed recently, such as PLSA. In this paper, we propose the use of a neural network based, non-probabilistic, solution, which captures jointly a rich representation of words and documents. Experiments performed on two information retrieval tasks using the TDT2 database and the TREC-8 and 9 sets of queries yielded a better performance for the proposed neural network model, as compared to PLSA and the classical TFIDF representations.
An erratum to this chapter can be found at http://dx.doi.org/10.1007/11550907_163 .
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References
Sebastiani, F.: Machine learning in automated text categorization. ACM Computing Surveys 34, 1–47 (2002)
Hofmann, T.: Unsupervised learning by Probabilistic Latent Semantic Analysis. Machine Learning 42, 177–196 (2001)
Le Cun, Y., Huang, F.J.: Loss functions for discriminative training of energy-based models. In: Proc. of AIStats (2005)
Salton, G., Wong, A., Yang, C.: A Vector Space Model for Automatic Indexing. Communication of the ACM 18 (1975)
Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Information Processing and Management 24, 513–523 (1988)
Deerwester, S.C., Dumais, S.T., Landauer, T.K., Furnas, G.W., Harshman, R.A.: Indexing by Latent Semantic Analysis. Journal of the American Society of Information Science 41, 391–407 (1990)
Gaussier, E., Goutte, C., Popat, K., Chen, F.: A hierarchical model for clustering and categorising documents. In: Crestani, F., Girolami, M., van Rijsbergen, C.J.K. (eds.) ECIR 2002. LNCS, vol. 2291, pp. 229–247. Springer, Heidelberg (2002)
Blei, D., Ng, A., Jordan, M.: Latent Dirichlet Allocation. JMLR 3, 993–1022 (2003)
Buntine, W.: Variational extensions to em and multinomial pca. In: Elomaa, T., Mannila, H., Toivonen, H. (eds.) ECML 2002. LNCS (LNAI), vol. 2430, pp. 23–34. Springer, Heidelberg (2002)
Keller, M., Bengio, S.: Theme topic mixture model: A graphical model for document representation. In: PASCAL Workshop on Learning Methods for Text Understanding and Mining (2004)
Cristianini, N., Shawe-Taylor, J., Lodhi, H.: Latent semantic kernels. J. Intell. Inf. Syst. 18, 127–152 (2002)
Bengio, Y., Ducharme, R., Vincent, P., Gauvin, C.: A Neural Probabilistic Language Model. JMLR 3, 1137–1155 (2003)
Collobert, R., Bengio, S.: Links between perceptrons, MLPs and SVMs. In: Proceedings of ICML (2004)
Lewis, D.D.: The trec-4 filtering track. In: TREC (1995)
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Keller, M., Bengio, S. (2005). A Neural Network for Text Representation. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3697. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550907_106
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DOI: https://doi.org/10.1007/11550907_106
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