A Literature Review and Discussion of Malay Rule - Based Affix Elimination Algorithms

Alfred, Rayner; Leong, Leow Ching; On, Chin Kim; Anthony, Patricia

doi:10.1007/978-94-007-7287-8_23

Cited by 6 publications

(2 citation statements)

References 5 publications

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“…The most pioneering stemmer for Malay is the Othman algorithm, which was proposed in 1993 [22]. This algorithm makes use of Kamus Dewan 1991, a Malaysian dictionary.…”

Section: Related Workmentioning

confidence: 99%

“…Even though there are plenty of rule-based stemming approaches for Malay that have been improved by the previous researchers since then, they still suffer from affixation errors, including over-stemming, understemming, unchanged, and spelling exceptions [19], [20], [21]. The major causes of this stemming error are the affix removal method, the similarity of the root word with the affixation www.ijacsa.thesai.org word, and exception rules in prefixation and confixation [22], [23].…”

Section: Introductionmentioning

confidence: 99%

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A Comparative Study of Stemming Techniques on the Malay Text

Mohemad,

Muhait,

Noor

et al. 2023

IJACSA

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Text stemming, an essential preprocessing step in the development of Natural Language Processing (NLP) applications, involves the transformation of various word forms into their root words. Stemming plays a critical role in decreasing the volume of text, thereby enhancing the efficiency of various computational tasks such as information retrieval, text classification, and text clustering. Stemming is a rule-based approach. On the other hand, it frequently suffers affixation errors that result in under-stemming, over-stemming, or both, as well as unstemmed or spelling exceptions. Every language has different stemming techniques, and among the most well-known Malay stemming algorithms are the Othman and Ahmad algorithms. Therefore, this study aims to compare the performance of the stemming errors between the Othman and Ahmad algorithms in stemming Malay text, particularly on two different domains of textual datasets, which are the course summaries of the education domain and housebreaking crime reports of the crime domain. The Othman algorithm presents a set of 121 stemming rules (set A). In the meantime, Ahmad's algorithm proposes two distinct sets of stemming rules, comprising 432 (set B) and 561 rules (set C), respectively. Based on the experiment results with 100 course summaries, the Ahmad algorithm (Set B) obtained a higher accuracy rate of 93.61%. The second highest is the Ahmad algorithm (Set C) with 93.53%. The Othman algorithm achieved the lowest accuracy with 86.04% compared to the other two algorithms. Meanwhile, findings from the experiment with 100 housebreaking crime reports show similar results, with the Ahmad algorithm (Set C) achieving the highest stemming accuracy of approximately 93.80% and the Othman algorithm producing the lowest stemming accuracy (83.09%). The result indicates that stemming accuracy is consistent across different types of datasets.

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“…The most pioneering stemmer for Malay is the Othman algorithm, which was proposed in 1993 [22]. This algorithm makes use of Kamus Dewan 1991, a Malaysian dictionary.…”

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Stemming Techniques on the Malay Text

Mohemad,

Muhait,

Noor

et al. 2023

IJACSA

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Enhanced Rules Application Order to Stem Affixation, Reduplication and Compounding Words in Malay Texts

Kassim

Maarof

Zainal

et al. 2016

Knowledge Management and Acquisition for Intelligent Systems

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A Literature Review on Text Classification and Sentiment Analysis Approaches

Wang

Alfred

Obit

et al. 2021

Lecture Notes in Electrical Engineering

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Sentiment analysis is an important branch task of text classification and the related system usually is applied to in perception of user emotion and public opinion monitoring. By comparison, the text classification can be applied to more fields than sentiment analysis. In the system architecture, same as text classification, the complete classification systemmainly contains data acquisition, data pre-process, feature extraction, classification algorithm and result output.TheWeb crawler usually be used in first step, the URL Link, hashtags, Non-Chinese text should be removed in second step. In feature extraction, the IG, TF-IDF, Word2vec usually be used. Then, the SVM, Naive Bayes, KNN or Neural network algorithm usually be used in classifier. Furthermore, as a system that can run automatically, the sentiment analysis system should be able to extract significant feature from corpus and make accurately analysis about emotional polarity of text corpus. At present, the system improvement direction of related system focuses on 3 aspects: data acquisition, feature extraction and classifier algorithm.

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A Literature Review and Discussion of Malay Rule - Based Affix Elimination Algorithms

Cited by 6 publications

References 5 publications

A Comparative Study of Stemming Techniques on the Malay Text

A Comparative Study of Stemming Techniques on the Malay Text

Enhanced Rules Application Order to Stem Affixation, Reduplication and Compounding Words in Malay Texts

A Literature Review on Text Classification and Sentiment Analysis Approaches

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