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https://doi.org/10.1109/uksim....
Article . 2014 . Peer-reviewed
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Arabic Text Root Extraction via Morphological Analysis and Linguistic Constraints

Authors: Alsaad, A; Abbod, M;

Arabic Text Root Extraction via Morphological Analysis and Linguistic Constraints

Abstract

Arabic language is vastly inflected, thus the process of effective Arabic text analysis with correct stem and root extraction is challenging. In this paper we present a linguistic root extraction approach that is composed of two main phases. In the first phase we handle removal of affixes including prefixes, suffixes and infixes. Prefixes and suffixes are removed depending on the length of the word, while checking its morphological pattern after each deduction to remove infixes. In the second phase, the root extraction algorithm is developed further to handle weak, hamzated, eliminated-long-vowel and two-letter geminated words as there is a rationally great amount of irregular Arabic words in texts. Before roots are extracted, they are checked against a predefined list of 3800 triliteral and 900 quad literal roots. Series of experiments has been conducted to improve and test the performance of the proposed algorithm. The obtained results revealed that the roots are extracted correctly has improved comparing with Khoja's stemming algorithm.

Country
United Kingdom
Related Organizations
Keywords

Text mining, Morphological analyser, Natural language processing, Arabic root extraction, Data mining

  • BIP!
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    citations
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
7
Average
Average
Average
Green