A hybrid model for Arabic document indexing

Guirat, Souheila Ben; Bounhas, Ibrahim; Slimani, Yahya

doi:10.1109/snpd.2016.7515887

Cited by 6 publications

(2 citation statements)

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“…After doing all the modifications in Table 1, the word is checked against several patterns like the Khoja stemmer with a slight difference. [25] use a modified version of the Ghwanmeh stemmer to extract three indexing terms: stem, verbed pattern, and root. For each word in a document, the three terms are extracted and indexed with their frequencies.…”

Section: Root Based Stemmingmentioning

confidence: 99%

“…They also concluded in their experiment that both roots based and light-based work well, but the root-based work better in high recall levels. [25]; [19] mentioned the same that using light based in indexing result in better precision, while the root based perform better in recall. [35] evaluated three stemming methods and suggested using hybrid method for Arabic language.…”

Section: Root Based Vs Light Basedmentioning

confidence: 99%

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Comparative Analysis and Evaluation of Stemming and Preprocessing Techniques for Arabic Text

Yaseen,

Al-Khulaidi

2023

jast

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Arabic information retrieval is challenging due to the language's complex morphology and syntax. Preprocessing and stemming improve the accuracy and efficiency of Arabic information retrieval. This paper provides a comprehensive analysis of the existing literature on Arabic preprocessing and stemming techniques. The paper identifies the limitations and challenges of these techniques. The paper emphasizes the importance of preprocessing and stemming and underscores the need for further research to improve Arabic information retrieval. This study evaluates ten stemmers on a public dataset. The results show that root-based stemmers: Lucene, and khoja got the highest reduction rate 90.9%, and 85% respectively. The results emphasize that root-based stemmers have good conflating ability for similar terms, while light-based stemmers under-stem similar terms.

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Section: Root Based Stemmingmentioning

confidence: 99%

Section: Root Based Vs Light Basedmentioning

confidence: 99%