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Arabic Stemmer Based Big Data

Arabic Stemmer Based Big Data

Youness Madani, Mohammed Erritali, Jamaa Bengourram
Copyright: © 2018 |Volume: 16 |Issue: 1 |Pages: 12
ISSN: 1539-2937|EISSN: 1539-2929|EISBN13: 9781522542360|DOI: 10.4018/JECO.2018010102
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MLA

Madani, Youness, et al. "Arabic Stemmer Based Big Data." JECO vol.16, no.1 2018: pp.17-28. http://doi.org/10.4018/JECO.2018010102

APA

Madani, Y., Erritali, M., & Bengourram, J. (2018). Arabic Stemmer Based Big Data. Journal of Electronic Commerce in Organizations (JECO), 16(1), 17-28. http://doi.org/10.4018/JECO.2018010102

Chicago

Madani, Youness, Mohammed Erritali, and Jamaa Bengourram. "Arabic Stemmer Based Big Data," Journal of Electronic Commerce in Organizations (JECO) 16, no.1: 17-28. http://doi.org/10.4018/JECO.2018010102

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Abstract

By its morphological and syntactic richness, the Arabic language is considered among the most difficult languages to deal with it in the field of information search. This is due; in particular, to the various difficulties encountered in its Stemming, which has not yet experienced a standard approach. The Stemming algorithm for Arabic words has been an important topic in Arabic information retrieval. The intention of this article is to parallelize a stemming algorithm for Arabic by proposing a distributed stemming algorithm in a big data system. This is by using the Hadoop framework, the MapReduce programming model for the development of the algorithm, and the distributed file system HDFS for the Storage of stemming result.

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