Abstract
Stemming is a process of reducing inflected words to their stem, stem or root from a generally written word form. One of the high inflected words in the languages world is Arabic Language. Stemming improve the retrieval performance by reducing words variants, and in lcrease the similarity between related words. However, an Arabic Information Retrieval (AIR) can use stemming algorithms to retrieve a greater number of documents related to the users’ query. Therefore, the aim of this paper is to evaluate the impact of three different Arabic stemmers (i.e. ‘Information Science Research Institute” (ISRI), morphological and syntax based lemmatization “Educated Text Stemmer” (ETS), and Light10 stemmer) on the Arabic Information Retrieval performance for Arabic language, we used the Linguistic Data Consortium (LDC) Arabic Newswire data set as benchmark dataset. The evaluation of the three different stemmers ranked the best performance was achieved by light10 stemmer in term of mean average precision.
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