Abstract
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.
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Wang, X., Kabán, A. (2006). Model-Based Estimation of Word Saliency in Text. In: Todorovski, L., Lavrač, N., Jantke, K.P. (eds) Discovery Science. DS 2006. Lecture Notes in Computer Science(), vol 4265. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893318_28
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DOI: https://doi.org/10.1007/11893318_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46491-4
Online ISBN: 978-3-540-46493-8
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