Model-Based Estimation of Word Saliency in Text
We investigate a generative latent variable model for model-based word saliency estimation for text modelling and classification. The estimation algorithm derived is able to infer the saliency of words with respect to the mixture modelling objective. We demonstrate experimental results showing that common stop-words as well as other corpus-specific common words are automatically down-weighted and this enhances our ability to capture the essential structure in the data, ignoring irrelevant details. As a classifier, our approach improves over the class prediction accuracy of the Naive Bayes classifier in all our experiments. Compared with a recent state of the art text classification method (Dirichlet Compound Multinomial model) we obtained improved results in two out of three benchmark text collections tested, and comparable results on one other data set.
KeywordsCommon Word Fundamental Freedom Essential Structure Irrelevant Detail Pluralistic Democracy
Unable to display preview. Download preview PDF.
- 1.Francis, W.N., Kucera, H.: Frequency analysis of English usage (1982)Google Scholar
- 4.McCallum, A., Nigam, K.: A comparison of event models for Naive Bayes text classification. In: AAAI 1998 Workshop on Learning for Text Categorization (1998)Google Scholar
- 5.Joachims, T.: Text Categorization with Support Vector Machines: Learning with Many Relevant Features. In: Proceedings of the European Conference on Machine Learning. Springer, Heidelberg (1998)Google Scholar
- 6.McCallum, A., Rosenfeld, R., Mitchell, T.M., Ng, A.Y.: Improving text classification by shrinkage in a hierarchy of classes. In: ICML 1998: Proceedings of the Fifteenth International Conference on Machine Learning, San Francisco, CA, USA, pp. 359–367. Morgan Kaufmann Publishers Inc., San Francisco (1998)Google Scholar
- 10.Yang, Y., Pedersen, J.O.: A comparative study on feature selection in text categorization. In: Fisher, D.H. (ed.) Proceedings of ICML 1997. 14th International Conference on Machine Learning, Nashville, US, pp. 412–420. Morgan Kaufmann Publishers, San Francisco (1997)Google Scholar