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Algorithmic stemmers or morphological analysis? An evaluation

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  • Claire Fautsch
  • Jacques Savoy

Abstract

It is important in information retrieval (IR), information extraction, or classification tasks that morphologically related forms are conflated under the same stem (using stemmer) or lemma (using morphological analyzer). To achieve this for the English language, algorithmic stemming or various morphological analysis approaches have been suggested. Based on Cross‐Language Evaluation Forum test collections containing 284 queries and various IR models, this article evaluates these word‐normalization proposals. Stemming improves the mean average precision significantly by around 7% while performance differences are not significant when comparing various algorithmic stemmers or algorithmic stemmers and morphological analysis. Accounting for thesaurus class numbers during indexing does not modify overall retrieval performances. Finally, we demonstrate that including a stop word list, even one containing only around 10 terms, might significantly improve retrieval performance, depending on the IR model.

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  • Claire Fautsch & Jacques Savoy, 2009. "Algorithmic stemmers or morphological analysis? An evaluation," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(8), pages 1616-1624, August.
    • Handle: RePEc:bla:jamist:v:60:y:2009:i:8:p:1616-1624
    DOI: 10.1002/asi.21093
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    Cited by:

    1. Jacques Savoy & Olena Zubaryeva, 2012. "Simple and efficient classification scheme based on specific vocabulary," Computational Management Science, Springer, vol. 9(3), pages 401-415, August.

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