Putting Successor Variety Stemming to Work

  • Benno Stein
  • Martin Potthast
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)


Stemming algorithms find canonical forms for inflected words, e. g. for declined nouns or conjugated verbs. Since such a unification of words with respect to gender, number, time, and case is a language-specific issue, stemming algorithms operationalize a set of linguistically motivated rules for the language in question. The most well-known rule-based algorithm for the English language is from [Porter (1980)].

The paper presents a statistical stemming approach which is based on the analysis of the distribution of word prefixes in a document collection, and which thus is widely language-independent. In particular, our approach tackles the problem of index construction for multi-lingual documents. Related work for statistical stemming either focuses on stemming quality (such as [Bachin et al. (2002) or Bordag (2005)]) or investigates runtime performance ([Mayfield and McNamee (2003)] for example), but neither provides a reasonable tradeoff between both. For selected retrieval tasks under vector-based document models we report on new results related to stemming quality and collection size dependency.

Interestingly, successor variety stemming has neither been investigated under similarity concerns for index construction nor is it applied as a technology in current retrieval applications. The results show that this disregard is not justified.


Categorization Task Vector Space Model Successor Variety Compound Word Document Model 
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 2007

Authors and Affiliations

  • Benno Stein
    • 1
  • Martin Potthast
    • 1
  1. 1.Faculty of Media, Media SystemsBauhaus University WeimarWeimarGermany

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