Improving the Arabic root extraction by using the quadratic splines

Boudchiche, Mohamed; Mazroui, Azzeddine

doi:10.1109/isacv.2018.8354062

Cited by 5 publications

(2 citation statements)

References 8 publications

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“…Without stemming, the dictionary interpretations of question terms were improbable to coordinate the shapes found in documents. Boudchiche and Mazroui (2018) clarified an Arabic root extraction framework that gives the root of each word of a given sentence. It is a critical instrument for a part of natural language preparing applications such as search engines, text classification, and information recovery.…”

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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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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“…First phase involves seeking all the possible roots of each term analyzed out of the context with a morphalizer. Second phase constructs a disambiguation approach based on continuous quadratic splines to choose among these roots the one that corresponds to the word context [29]. Momani and Faraj [30] filters rootless words, and then removes suffixes and prefixes.…”

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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Enhancing Machine Translation by Integrating Linguistic Knowledge in the Word Alignment Module

Berrichi

Mazroui

2020

2020 International Conference on Intelligent Systems and Computer Vision (ISCV)

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Improving the Arabic root extraction by using the quadratic splines

Cited by 5 publications

References 8 publications

Text mining: A survey of Arabic root extraction algorithms

Text mining: A survey of Arabic root extraction algorithms

Root Identification Tool for Arabic Verbs

Enhancing Machine Translation by Integrating Linguistic Knowledge in the Word Alignment Module

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