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GRAS: An Effective and Efficient Stemming Algorithm for Information Retrieval
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AI
DOI：10.1145/2037661.2037664.png)
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Abstract
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A novel graph-based language-independent stemming algorithm suitable for information retrieval is proposed in this article. The main features of the algorithm are retrieval effectiveness, generality, and computational efficiency. We test our approach on seven languages (using collections from the TREC, CLEF, and FIRE evaluation platforms) of varying morphological complexity. Significant performance improvement over plain word-based retrieval, three other language-independent morphological normalizers, as well as rule-based stemmers is demonstrated.
Keywords:
Algorithms
Experimentation
Performance
Corpus
stemming
suffix
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9.1
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