Arabic root extraction using a hybrid technique

Khafajeh, Hayel; Yousef, Nidal; Abdeldeen, Mahmoud

doi:10.19101/ijacr.2017.733023

Cited by 6 publications

(6 citation statements)

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“…Moreover, there are some research works conducted on the Arabic root's extraction, for instance, a multi-objective method with a statistical method to separate the suggested Arabic roots. The results presented that the developed method improved the performance of extracting the Arabic roots [13]. Besides, Yousef et al proposed an approach to improve the Arabic root extraction method for all words, according to the bi-gram technique.…”

Section: Literature Reviewmentioning

confidence: 98%

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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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Section: Literature Reviewmentioning

confidence: 98%

“…Bi-gram [13], [14] The multi-objective method with a statistical method to separate the Arabic roots suggested in [13] is improved in [14] using the bi-gram technique.…”

Section: %mentioning

confidence: 99%

Arabic Text Processing Model: Verbs Roots and Conjugation Automation

2020

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“…This algorithm has been tested on a corpus of 72 abstracts (10582 words) from the Saudi Arabian National Computer Conference; the algorithm accuracy is approximately 92%. Khafajeh et al (2018) explained a crossover strategy to extricate Arabic word roots had been creating. The proposed method depends on optimization work, which is the improving operation performed by playing a set of non-morphological rules to improve the n-gram method.…”

Section: Root Extraction Algorithmmentioning

confidence: 99%

Text mining: A survey of Arabic root extraction algorithms

al.¹

2021

Int. j. adv. appl. sci.

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In all Arab countries, the Arabic language is the official language spoken and written and is one of the oldest known languages. This paper aims to explain and discuss the work done on extracting the root of the Arabic word and Stemming algorithms. Text mining has become of interest to scientists, researchers, and users because of the existence of big data and deep learning algorithms that can analyze giant sets of unstructured data. The basic algorithms are used to extract and classify texts, information retrieval systems, and indexes. Algorithms are used to extract the root of a word from different natural languages. This paper will present a brief background and comprehensive presentation of a number of algorithms that handle the Arabic text to extract the word root in its light, heavy, hybrid, leading, and Markovian form. There are a number of papers, articles, and research papers that deal with extracting the Arabic root from the word. This paper will present a brief background for a number of stemming algorithms on how to extracting the root and stem of the Arabic word, then make a comparison and discussion of a number of selected algorithms in terms of accuracy, data set, method of stemming regarding of strengths and weakness.

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“…In general, extracting root systems are mostly relying on rule-based approach [36], which in turn has deficiencies in tackling abundance of Arabic language inflections 1 https://www.almaany.com/ar/dict/ar-ar/?c=%D9%85%D8%AE%D8% AA%D8%A7%D8%B1%20%D8%A7%D9%84%D8%B5%D8%AD% D8%A7%D8%AD c and derivations. Since most of the rule-based Arabic stemmers go to remove prefixes, infixes, and suffixes, most of these stemmers cannot not recognize the right root of some Arabic terms, i.e.…”

Section: Related Workmentioning

confidence: 99%

Root Identification Tool for Arabic Verbs

Azman

2019

IEEE Access

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Numerous Arabic morphology systems have been devoted toward morphed requirements of words that are required by other text analyzers. Term rooting is an essential requirement in those systems, yet rooting module in the state-of-the-art morphology systems insufficiently meet that requirement, especially verb term. Consequently, due to termination in stemming term rather than a rooting term. Since the stem of the verb is not the root of the verb, it is not feasible to generate or inference verb's derivations and whole it's surface forms (patterns) such tense, number, mood, person, aspect, and others of verb irregular patterns. Therefore, we propose a new model for identifying the verb's root produced in a tool (RootIT) in order to overcome verb root extraction without disambiguation out of traditional methods, applied in current morphology systems. A major design goal of this system is that it can be used as a standalone tool and can be integrated, in a good manner, with other linguistic analyzers. The adopted approach is a mapping surface verb with full-scale derivative verbs discharged previously in the relational database. Moreover, the proposed system is tested on the adopted dataset from PATB verbs extracted from CoreNLP system. The extracted dataset, containing more than (7950) distinguishes verbs belonging to (1938) different roots. The results obtained outstrip the best-compared system by (2.74%) of high accuracy.

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Arabic root extraction using a hybrid technique

Cited by 6 publications

References 3 publications

Arabic Text Processing Model: Verbs Roots and Conjugation Automation

Arabic Text Processing Model: Verbs Roots and Conjugation Automation

Text mining: A survey of Arabic root extraction algorithms

Root Identification Tool for Arabic Verbs

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