Arabic light-based stemmer using new rules

Alshalabi, Hamood; Tiun, Sabrina; Omar, Nazlia; AL-Aswadi, Fatima N.; Alezabi, Kamal Ali

doi:10.1016/j.jksuci.2021.08.017

Cited by 20 publications

(9 citation statements)

References 16 publications

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“…In Figure [8], Stop word removal is the procedure of getting rid of words that aren't thought to be significant. Whether a word contains a stop word or not, the data dictionary serves as a useful reference [21].…”

Section: Removed Stop Wordmentioning

confidence: 99%

A smart model to minimize the waiting time for the cases in Egyptian Economic courts

توفيق,

Mohamed Bahloul

2024

المجلة العلمية للبحوث والدراسات التجارية

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The economic courts in Egypt play a crucial role in resolving disputes related to economic and commercial matters. These cases cover a wide range of issues that require timely and cost-effective resolutions to ensure the effective management of huge construction projects in Egypt. Machine learning plays a crucial role in the cases handled by Egyptian Economic Courts. It helps in the analysis of large amounts of data, identifying patterns and trends, and making predictions based on historical data. The research delves into the application of various algorithms to identify similar court rulings within Arabic texts, using a dataset comprising 3633 court rulings from the Economic Court in Egypt. The employment of various algorithms has facilitated the efficient selection of comparable court rulings, thus expediting the process of deriving effective conclusions within a minimal timeframe. The evaluation of classifier performance was conducted using metrics such as accuracy, precision, recall, and the F1 score. Notably, the

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Section: Removed Stop Wordmentioning

confidence: 99%

A smart model to minimize the waiting time for the cases in Egyptian Economic courts

توفيق,

Mohamed Bahloul

2024

المجلة العلمية للبحوث والدراسات التجارية

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“…[5] proposed the AMIR stemmer, which can remove infixes along with prefixes and suffixes to extract the stem of the word. Finally, [33] proposed a stemmer called D-light stemmer, which have a list for definite articles, suffixes, and prefixes. This stemmer applies several rules based on word length to remove the definite article and suffixes before removing prefixes as shown in Table 3.…”

Section: Light Based Stemmingmentioning

confidence: 99%

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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“…Upon examining Thalji et al's corpus [7], the roots can be sorted into categories based on their length and the type of letters they contain, as illustrated in Table I. 2) Arabic affixes: Arabic prefixes are added at the beginning of a root word to modify its meaning or function, and can indicate various aspects of meaning [25]. Thalji et al's corpus includes 430 prefixes, with lengths ranging from one to six letters, and Table II provides statistical information on a subset of these prefixes.…”

Section: A the Datasetmentioning

confidence: 99%

New Arabic Root Extraction Algorithm

Thalji,

Aljarrah,

Rateb

et al. 2023

IJACSA

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This research presents a new algorithm for Arabic root extraction, which aims to improve the accuracy of Arabic Natural Language Processing Algorithms by addressing the weaknesses and errors of existing algorithms. The proposed algorithm utilizes a database, that includes a collection of roots, patterns, and affixes, to generate potential derivation roots of a word without eliminating affixes initially. By matching the derived word with patterns to identify potential roots, the proposed algorithm avoids the inaccuracies caused by eliminating affixes based on expectation methods. The study conducted a comparison of the proposed algorithm with three commonly used Arabic root extraction algorithms. The evaluation process is performed on three corpora. Resultsshowed that the proposed algorithm achieved an average accuracy rate of 96%, which is significantly higher than the others.

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Arabic light-based stemmer using new rules

Cited by 20 publications

References 16 publications

A smart model to minimize the waiting time for the cases in Egyptian Economic courts

A smart model to minimize the waiting time for the cases in Egyptian Economic courts

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

New Arabic Root Extraction Algorithm

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