Root Identification Tool for Arabic Verbs

Azman, Bakeel

doi:10.1109/access.2019.2908177

Cited by 7 publications

(7 citation statements)

References 21 publications

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“…This is useful for text analysis tasks such as information retrieval and text classification. It is necessary to get acquainted with some of the basic concepts used in root extraction processes to clarify and remove ambiguity [7]. The lemmatization process entails identifying each word of the text and linking it to a specific canonical word.…”

Section: Issn: 0067-2904mentioning

confidence: 99%

“…3.Stemming: It is the process of reducing a word to its stem form, typically by removing prefixes and suffixes [7]. For example, in the word " ‫بُ‬ ِ ‫ْر‬ ‫ُش‬ ‫"ي‬ (yushribu) which means "he drinks," the stem of this word is ‫"شرب"‬ (sh-r-b), which is the root of the word, and the inflectional suffixes ‫"ي"‬ (y) and " ُُ" (u) have been removed.…”

Section: Issn: 0067-2904mentioning

confidence: 99%

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A Survey of Arabic Root Extraction Algorithms

Thalji

2024

Iraqi Journal of Science

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The importance of a survey of Arabic root extraction algorithms is that it can provide a comprehensive overview of the current state of the field and the various approaches used for this task. This can be helpful for academics and professionals working on Arabic natural language processing projects, since it enables them to comprehend the advantages and disadvantages of several approaches and select the one that is best suited to the task at hand. A survey can also help to pinpoint areas that require more research and can serve as a springboard for fresh studies in this area. In general, a survey can enhance the study of Arabic root extraction and improve the quality of Arabic natural language processing tools. Researchers can use this survey to compare the performance of several algorithms and select the best one for their particular application. In this survey, the majority of the root extraction algorithms for the Arabic language were studied with a focus on identifying their advantages and disadvantages, as well as their accuracy. The algorithms were classified into various approaches. The importance of root extraction in Arabic is explored, along with its uses and applications. Also, the issues that still require research and improvement in order to improve the efficiency of algorithms were highlighted. For instance, one issue that the algorithms encounter is the absence of a unified list that encompasses all roots, patterns, and affixes. Additionally, more rules are needed to regulate the process of root extraction.

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Section: Issn: 0067-2904mentioning

confidence: 99%

Section: Issn: 0067-2904mentioning

confidence: 99%

A Survey of Arabic Root Extraction Algorithms

Thalji

2024

Iraqi Journal of Science

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“…Al. [42] constructed the tree structure, in which the center of the tree depicts the root verb and child nodes show its variant forms. Kaur and Preetpal Kaur Buttar [34] developed the 3,135 Punjabi root verbs and suffix removal rules.…”

Section: A Linguistic Knowledge-based Approachesmentioning

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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“…The resource building process has been implemented over three steps: -verbs resource: we have built a comprehensive verbs resource. It consists of a dictionary containing 295 possible verbs representative models [18]. Each verb is linked to its morpho-phonological, morphosyntactical, and semantic features.…”

Section: Contributionmentioning

confidence: 99%

The use of Arabic linguistic resources to develop learning applications

Blanchete

Mourchid

2022

IJEECS

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<span>This paper aims at explaining how Arabic linguistic resources are generated and exploited to enhance Arabic acquisition. We have adopted the root and pattern approach to generate our resources using the linguistic NooJ platform. This work has been carried out in two phases: generating the linguistic resources and developing the application that exploits the pre-built resources. First, we have generated three different resources: comprehensive verbs and masdar resources linked to each other. A nouns-and-adjectives resource, where nouns and adjectives are linked to their broken plural forms. NooJ calls these resources to apply linguistic analysis to a given corpus and returns detailed annotations, which provides accurate morphological, syntactical, and semantic properties of each analyzed word. We have also used the mixed nature of Arabic masdar to implement transformational rules, which generate nominal sentences from verbal ones and vice versa. Second, we have developed an application that provides valuable learning functionalities, like full/semi verb conjugation, the extraction of broken plural forms of a given singular form, the extraction of masdar forms of a given verb form, and return words that share the same root. The developed application can be used by teachers, students/learners, and computational linguists interested in Arabic acquisition.</span>

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Root Identification Tool for Arabic Verbs

Cited by 7 publications

References 21 publications

A Survey of Arabic Root Extraction Algorithms

A Survey of Arabic Root Extraction Algorithms

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

The use of Arabic linguistic resources to develop learning applications

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