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Algorithmic stemmers or morphological analysis? An evaluation
Auteur(s)
Fautsch, Claire
Maison d'édition
Wiley
Date de parution
2009
In
Journal of the American Society for Information Science and Technology
Vol.
60
No
6
De la page
1616
A la page
1624
Résumé
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.
Identifiants
Type de publication
journal article
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