Inference and Evaluation of the Multinomial Mixture Model for Text Clustering

Computer Science – Information Retrieval

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

Scientific paper

10.1016/j.ipm.2006.11.001

In this article, we investigate the use of a probabilistic model for unsupervised clustering in text collections. Unsupervised clustering has become a basic module for many intelligent text processing applications, such as information retrieval, text classification or information extraction. The model considered in this contribution consists of a mixture of multinomial distributions over the word counts, each component corresponding to a different theme. We present and contrast various estimation procedures, which apply both in supervised and unsupervised contexts. In supervised learning, this work suggests a criterion for evaluating the posterior odds of new documents which is more statistically sound than the "naive Bayes" approach. In an unsupervised context, we propose measures to set up a systematic evaluation framework and start with examining the Expectation-Maximization (EM) algorithm as the basic tool for inference. We discuss the importance of initialization and the influence of other features such as the smoothing strategy or the size of the vocabulary, thereby illustrating the difficulties incurred by the high dimensionality of the parameter space. We also propose a heuristic algorithm based on iterative EM with vocabulary reduction to solve this problem. Using the fact that the latent variables can be analytically integrated out, we finally show that Gibbs sampling algorithm is tractable and compares favorably to the basic expectation maximization approach.

No associations

LandOfFree

Say what you really think

Search LandOfFree.com for scientists and scientific papers. Rate them and share your experience with other people.

Rating

Inference and Evaluation of the Multinomial Mixture Model for Text Clustering does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Inference and Evaluation of the Multinomial Mixture Model for Text Clustering, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Inference and Evaluation of the Multinomial Mixture Model for Text Clustering will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-196950

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.