Formalizing Arabic Inflectional and Derivational Verbs Based on Root and Pattern Approach Using NooJ Platform

Blanchete, Ilham; Mourchid, Mohammed; Mbarki, Samir; Mouloudi, Abdelaziz

doi:10.1007/978-3-319-73420-0_5

Cited by 8 publications

(7 citation statements)

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“…They usually employ the statistical approach or adopt the linguistic approach, but they neglige the Arabic language structure and adopt the lemma rather than root and pattern morphology, which makes extracting linguistic properties like roots and patterns hard to execute. Patterns also play an important role in Arabic morphology, e.g., we can extract words concepts from patterns [4], [10]. A case in point, the pattern (FiEaALaa-‫ة‬ َ ‫ال‬ َ ‫ِع‬ ‫)ف‬ is employed to extract craft concept, generally.…”

Section: Contributionmentioning

confidence: 99%

“…To clarify, conditions have been tested on the linguistic properties, which have been accurately assigned to dictionary entries. E.g., broken plural and masdar patterns [4]. Thus, we cannot avoid vocabulary roots, patterns, morphophonological, morphosyntactical, and semantic features during the formalization of the Arabic language.…”

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confidence: 99%

“…Like rules have been used for the implementation of Arabic phonological rules [9], formalization of the Arabic grammatical category (V-a) [7], lexicon-grammar tables development for Arabic psychological verbs, applying transformational grammars to recognize Arabic psychological verbs [9]. works mentioned in [4]- [9] have been realized with great attention to the Arabic root and pattern morphology. Hence, developers can rely on these works to develop NLP applications.…”

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confidence: 99%

“…Another work, "EL-DiCar" dictionary [3], which adopts the linguistic approach, but has shown several weaknesses in advanced analysis phases. Dictionaries that adopt the lemma approach to formalize the Arabic Semitic language, and ignore the root-pattern during the dictionary construction, are unable to: i) extract meaning using patterns [4], ii) extract words that share the same root [10], and iii) unable to generate the broken plurals and masdar forms from their singular linguistic properties; since no root and pattern has been assigned to dictionary entries [4]; instead, they assign the regular plural form for each singular one, if any. Besides root and pattern, some phonological, morphological, syntactical, and semantic features have been ignored during "EL-DiCar" construction, which is unacceptable since Arabic is Semitic [11].…”

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confidence: 99%

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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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Section: Contributionmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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The use of Arabic linguistic resources to develop learning applications

Blanchete

Mourchid

2022

IJEECS

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“…The primary process concentrated on the roots classification and matching with patterns to give lemma. The output of this work introduced a dictionary that includes a big set of inflectional and derivational verbs styles [28].…”

Section: Literature Reviewmentioning

confidence: 99%

Arabic Text Processing Model: Verbs Roots and Conjugation Automation

2020

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The Natural Language Processing (NLP) is a process to automate the text or speech of Natural Languages. This automation is mainly conducted for Western languages. The Arabic Language got less focus in this area. This paper presents a Model to recognize an Arabic sentence. A new morphological model based on regular expressions is developed to recognize the Arabic verbs. A hash table containing all Arabic three-letters' root of verbs is implemented. The total number of Arabic verbs that are derived from three-letters' root size is 23090. The number of roots is 6104. A set of rules forming the Arabic grammar is used to derive and analyze the syntax of Arabic sentences. About 87% of the verbs represented in our regular expressions' engine are detected. Moreover, the sentences are also recognized. In several Surat of the Quran, only 9% of the detected verbs are false-positive (a non-verb declared as a verb), and 4% are considered false-negative (a verb is considered as a noun). This rate is mainly because we are not using vowels even that the Quran (our case study) is using them. The reason behind our decision is to be able to handle all Arabic texts, which mostly are not using vowels.

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