Open Access

ARTICLE

A New Enhanced Arabic Light Stemmer for IR in Medical Documents

Ra’ed M. Al-Khatib^1,*, Taha Zerrouki², Mohammed M. Abu Shquier³, Amar Balla⁴, Asef Al-Khateeb⁵

1 Department of Computer Sciences, Yarmouk University, Irbid, 21163, Jordan
2 Faculty of Sciences and Applied Sciences, Bouira University, Bouira, Algeria
3 Faculty of Computer Science and Information Technology, Jerash University, Jordan
4 Ecole Nationale Supérieure d’Informatique (ESI), Algiers, Algeria
5 Department of Computer Science, College of Shari’a and Islamic Studies in Al Ahsaa, Imam Mohammad Ibn Saud Islamic University (IMSIU), Saudi Arabia

* Corresponding Author: Ra’ed M. Al-Khatib. Email:

(This article belongs to the Special Issue: Artificial Intelligence and IoT based intelligent systems using high performance computing for Medical applications.)

Computers, Materials & Continua 2021, 68(1), 1255-1269. https://doi.org/10.32604/cmc.2021.016155

Received 25 December 2020; Accepted 03 February 2021; Issue published 22 March 2021

Abstract

This paper introduces a new enhanced Arabic stemming algorithm for solving the information retrieval problem, especially in medical documents. Our proposed algorithm is a light stemming algorithm for extracting stems and roots from the input data. One of the main challenges facing the light stemming algorithm is cutting off the input word, to extract the initial segments. When initiating the light stemmer with strong initial segments, the final extracting stems and roots will be more accurate. Therefore, a new enhanced segmentation based on deploying the Direct Acyclic Graph (DAG) model is utilized. In addition to extracting the powerful initial segments, the main two procedures (i.e., stems and roots extraction), should be also reinforced with more efficient operators to improve the final outputs. To validate the proposed enhanced stemmer, four data sets are used. The achieved stems and roots resulted from our proposed light stemmer are compared with the results obtained from five other well-known Arabic light stemmers using the same data sets. This evaluation process proved that the proposed enhanced stemmer outperformed other comparative stemmers.

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Keywords

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