Enhanced Algorithm for Extracting the Root of Arabic Words

Ghwanmeh, Sameh; Kanaan, Ghassan; Al-Shalabi, Riyad; Rabab'ah, Saif

doi:10.1109/cgiv.2009.10

Cited by 53 publications

(66 citation statements)

References 5 publications

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“…The second class is formed of statistical methods which are simple, fast, and do not require any morphological rules but some calculations [14,15,16,17,18,19,20].…”

Section: Introductionmentioning

confidence: 99%

Arabic Texts Categorization: Features Selection Based on the Extraction of Words’ Roots

Gadri

Moussaoui

2015

IFIP Advances in Information and Communication Technology

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“…The second class is formed of statistical methods which are simple, fast, and do not require any morphological rules but some calculations [14,15,16,17,18,19,20].…”

Section: Introductionmentioning

confidence: 99%

Arabic Texts Categorization: Features Selection Based on the Extraction of Words’ Roots

Gadri

Moussaoui

2015

IFIP Advances in Information and Communication Technology

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“…Light stemmer by Naglaa Thabet [29] used only in information retrieval and text classification. Light10, Rule-based Light Stemmers by Kanaan [20], Enhanced algorithm for extracting the root of Arabic words by Ghwanmeh [15] were not cited in this time period.…”

Section: The Usages In the Different Fields Of Nlp Domainsmentioning

confidence: 99%

“…Enhanced Algorithm for Extracting the Root of Arabic Words by Ghwanmeh et al [15] The stemmer is based on the roots of the Arabic word. It extracts the Arabic roots from the Arabic words.…”

Section: The Isri Arabic Stemmer Bymentioning

confidence: 99%

“…The ISRI Arabic Stemmer was used most in information retrieval then text classification, information extraction, text mining, Q&A and text clustering. Enhanced algorithm for extracting the root of Arabic words by Ghwanmeh [15] was used most in information retrieval then text indexing and finally information extraction and text classification. Buckwalter used most in machine translation the most then automatic speech recognition then information retrieval, extraction, text mining and Q&A.…”

Section: The Usages In the Different Fields Of Nlp Domainsmentioning

confidence: 99%

“…It compares between the following twenty stemmers: Arabic stemmer [23], AlKhalil morphological analyzer [9], SAFAR [17], Arabic Stemming with two dictionaries [21], new version of the Arabic Morphological analyzer MORPH2 [19], light-stemming algorithm [2], the light stemmer [23], Light8 [25], Light10 [24], Berkeley light stemmer [10], Al-Stem Stemmer [11], SP_WOAL Arabic Light Stemmer [1], Rule-based Light Stemmers [20], Linguistic-based stemmer [18], DomainSpecific Arabic Stemmer [12], The ISRI Arabic Stemmer [28], Enhanced algorithm for extracting the root of Arabic words [15], Buckwalter Arabic Morphological Analyzer (BAMA), and light stemmer [29]. This study is comparing Arabic stemmers based on the following aspects:…”

Section: The Criteria Of Comparisonmentioning

confidence: 99%

See 2 more Smart Citations

A Comparative Study on Arabic Stemmers

Dahab¹,

Ibrahim²,

Al-Mutawa³

2015

IJCA

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Stemming is considered as a pre-processing step in many applications: text mining, information retrieval, machine translation etc. The Arabic language has many special cases or properties that affect stemming or any automatic method, it depends on both inflectional and derivational morphology to produce the various forms of the language words. Many researchers have proposed algorithms to solve the problems of stemming. This paper aims to make a comparison study among the existing Arabic stemmers, the comparison study is based on the methodologies, the usage, main idea, algorithm, the affixes, limitations, output, and the stemmers' sensitivity for both diacritics and context. General TermsNatural Language Processing.

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A comparison study of some Arabic root finding algorithms

Al-Shawakfa

Al-Badarneh

Shatnawi

et al. 2010

J. Am. Soc. Inf. Sci.

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Arabic has a complex structure, which makes it difficult to apply natural language processing (NLP). Much research on Arabic NLP (ANLP) does exist; however, it is not as mature as that of other languages. Finding Arabic roots is an important step toward conducting effective research on most of ANLP applications. The authors have studied and compared six root-finding algorithms with success rates of over 90%. All algorithms of this study did not use the same testing corpus and/or benchmarking measures. They unified the testing process by implementing their own algorithm descriptions and building a corpus out of 3823 triliteral roots, applying 73 triliteral patterns, and with 18 affixes, producing around 27.6 million words. They tested the algorithms with the generated corpus and have obtained interesting results; they offer to share the corpus freely for benchmarking and ANLP research.

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Enhanced Algorithm for Extracting the Root of Arabic Words

Cited by 53 publications

References 5 publications

Arabic Texts Categorization: Features Selection Based on the Extraction of Words’ Roots

Arabic Texts Categorization: Features Selection Based on the Extraction of Words’ Roots

A Comparative Study on Arabic Stemmers

A comparison study of some Arabic root finding algorithms

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