Probabilistic models for text mining

Yizhou Sun, Hongbo Deng, Jiawei Han

Research output: Chapter in Book/Report/Conference proceedingChapter

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.

Original languageEnglish (US)
Title of host publicationMining Text Data
PublisherSpringer US
Pages259-295
Number of pages37
Volume9781461432234
ISBN (Electronic)9781461432234
ISBN (Print)1461432227, 9781461432227
DOIs
StatePublished - Aug 1 2012

Keywords

  • Graphical model
  • Mixture model
  • Probabilistic models
  • Stochastic process

ASJC Scopus subject areas

  • Computer Science(all)

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  • Cite this

    Sun, Y., Deng, H., & Han, J. (2012). Probabilistic models for text mining. In Mining Text Data (Vol. 9781461432234, pp. 259-295). Springer US. https://doi.org/10.1007/978-1-4614-3223-4_8