A Study of Graph Based Stemmer in Arabic Extrinsic Plagiarism Detection

Boukhalfa, Imene; Mostefai, Sihem; Chekkai, Nacira

doi:10.1145/3177148.3180089

Cited by 3 publications

(1 citation statement)

References 8 publications

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“…Some stemmers are developed for specific NLP applications such as [73] constructed stemmer for IR systems, Boukhalfa et al [75] stemmer developed for the improvement of the accuracy of plagiarism detection, and Ali et al [47] developed stemmer for text classifications. The performance of a stemmer may vary from one NLP application to another.…”

Section: Task Factormentioning

confidence: 99%

An Analytical Analysis of Text Stemming Methodologies in Information Retrieval and Natural Language Processing Systems

Jabbar,

Iqbal,

Tamimy

et al. 2023

IEEE Access

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The exponential increase in textual unstructured digital data creates significant demand for advanced and smart stemming systems. As a preprocessing stage, stemming is applied in various research fields such as information retrieval (IR), domain vocabulary analysis, and feature reduction in many natural language processing (NLP). Text stemming (TS), an important step, can significantly improve performance in such systems. Text-stemming methods developed till now could be better in their results and can produce errors of different types leading to degraded performance of the applications in which these are used. This work presents a systematic study with an in-depth review of selected stemming works published from 1968 to 2023. The work presents a multidimensional review of studied stemming algorithms i.e., methodology, data source, performance, and evaluation methods. For this study, we have chosen different stemmers, which can be categorized as 1) linguistic knowledge-based, 2) statistical, 3) corpus-based, 4) context-sensitive, and 5) hybrid stemmers. The study shows that linguistic knowledge-based stemming techniques were widely used for highly inflected languages (such as Arabic, Hindi, and Urdu) and have reported higher accuracy than other techniques. We compare and analyze the performance of various state-of-the-art TS approaches, including their issues and challenges, which are summarized as research gaps. This work also analyzes different NLP applications utilizing stemming methods. At the end, we list the future work directions for interested researchers.

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Section: Task Factormentioning

confidence: 99%

An Analytical Analysis of Text Stemming Methodologies in Information Retrieval and Natural Language Processing Systems

Jabbar,

Iqbal,

Tamimy

et al. 2023

IEEE Access

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Empirical evaluation and study of text stemming algorithms

et al. 2020

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A Systematic Review of Stemmers of Indian and Non-Indian Vernacular Languages

Dave

Mehta²,

Kotecha

2024

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

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The stemming process is crucial and significant in the pre-processing step of natural language processing. The stemmer oversees the stemming process. It facilitates the extraction of morphological variants of a root or base word from the provided word. Over the period, several stemmers for various vernacular languages have been proposed. However, very few research studies have comprehensively investigated these available stemmers. This paper makes multifold contributions. First, we discuss the various stemmers of 15 Indian and 17 non-Indian languages describing their key points, benefits, and drawbacks. All the Indian languages for which stemmers have been built are covered in this study. For the non-Indian languages, stemmers of commonly spoken languages have been covered. Second, we present a language-wise comparative analysis of stemmers based on our identified parameters. Third, we discuss the wordnets and dictionaries available for different languages. Fourth, we provide details of the datasets available for various languages. Fifth, we also provide challenges in existing stemmers and future directions for future researchers. The study presented in this paper reveals that significant research has been carried out for the stemmers of influential languages such as English, Arabic, and Urdu. On the other hand, languages with limited resources, such as Farsi, Polish, Odia, Amharic, and others, have received the least attention for research. Moreover, rigorous analysis reveals that most of the stemmers suffer from over-stemming errors. With a complete catalogue of available stemmers, this study aims to assist the researchers and professionals working in the areas such as information retrieval, semantic annotation, word meaning disambiguation, and ontology learning.

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A Study of Graph Based Stemmer in Arabic Extrinsic Plagiarism Detection

Cited by 3 publications

References 8 publications

An Analytical Analysis of Text Stemming Methodologies in Information Retrieval and Natural Language Processing Systems

An Analytical Analysis of Text Stemming Methodologies in Information Retrieval and Natural Language Processing Systems

Empirical evaluation and study of text stemming algorithms

A Systematic Review of Stemmers of Indian and Non-Indian Vernacular Languages

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