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Distribution Based Stemmer Refinement

  • B. L. Narayan
  • Sankar K. Pal
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3776)

Abstract

Stemming is a common preprocessing task applied to text corpora. Errors in this process may be refined either manually or based on a corpus. We describe a novel corpus-based stemming technique which models the given words as being generated from a multinomial distribution over the topics available in the corpus. A sequential hypothesis testing like procedure helps us group together distributionally similar words. This stemmer refines any given stemmer and its strength can be controlled with the help of two thresholds. A refinement based on the 20 Newsgroups data set shows that the proposed method splits equivalence classes appropriately.

Keywords

Equivalence Class Multinomial Distribution Vector Space Model Similar Word Text Corpus 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • B. L. Narayan
    • 1
  • Sankar K. Pal
    • 1
  1. 1.Machine Intelligence UnitIndian Statistical InstituteCalcuttaIndia

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