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Probabilistic Models for Text Mining

  • Yizhou SunEmail author
  • Hongbo Deng
  • Jiawei Han
Chapter

Abstract

A number of probabilistic methods such as LDA, hidden Markov models, Markov random fields have arisen in recent years for probabilistic analysis of text data. This chapter provides an overview of a variety of probabilistic models for text mining. The chapter focuses more on the fundamental probabilistic techniques, and also covers their various applications to different text mining problems. Some examples of such applications include topic modeling, language modeling, document classification, document clustering, and information extraction.

Keywords

Probabilistic models mixture model stochastic process graphical model 

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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of Illinois at Urbana-ChampaignChampaignUSA

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