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Arabic nested noun compound extraction based on linguistic features and statistical measures

The extraction of Arabic nested noun compound is significant for several research areas such as sentiment analysis, text summarization, word categorization, grammar checker, and machine translation. Much research has studied the extraction of Arabic noun compound using linguistic approaches, stat...

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Main Authors: Nazlia Omar,, Qasem Al-Tashi,
Format: Article
Language:English
Published: Penerbit Universiti Kebangsaan Malaysia 2018
Online Access:http://journalarticle.ukm.my/13773/1/25313-76332-1-PB.pdf
http://journalarticle.ukm.my/13773/
http://ejournal.ukm.my/gema/issue/view/1087
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spelling my-ukm.journal.137732019-12-09T23:10:45Z http://journalarticle.ukm.my/13773/ Arabic nested noun compound extraction based on linguistic features and statistical measures Nazlia Omar, Qasem Al-Tashi, The extraction of Arabic nested noun compound is significant for several research areas such as sentiment analysis, text summarization, word categorization, grammar checker, and machine translation. Much research has studied the extraction of Arabic noun compound using linguistic approaches, statistical methods, or a hybrid of both. A wide range of the existing approaches concentrate on the extraction of the bi-gram or tri-gram noun compound. Nonetheless, extracting a 4-gram or 5-gram nested noun compound is a challenging task due to the morphological, orthographic, syntactic and semantic variations. Many features have an important effect on the efficiency of extracting a noun compound such as unit-hood, contextual information, and term-hood. Hence, there is a need to improve the effectiveness of the Arabic nested noun compound extraction. Thus, this paper proposes a hybrid linguistic approach and a statistical method with a view to enhance the extraction of the Arabic nested noun compound. A number of pre-processing phases are presented, including transformation, tokenization, and normalisation. The linguistic approaches that have been used in this study consist of a part-of-speech tagging and the named entities pattern, whereas the proposed statistical methods that have been used in this study consist of the NC-value, NTC-value, NLC-value, and the combination of these association measures. The proposed methods have demonstrated that the combined association measures have outperformed the NLC-value, NTC-value, and NC-value in terms of nested noun compound extraction by achieving 90%, 88%, 87%, and 81% for bigram, trigram, 4-gram, and 5-gram, respectively. Penerbit Universiti Kebangsaan Malaysia 2018-05 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/13773/1/25313-76332-1-PB.pdf Nazlia Omar, and Qasem Al-Tashi, (2018) Arabic nested noun compound extraction based on linguistic features and statistical measures. GEMA: Online Journal of Language Studies, 18 (2). pp. 93-107. ISSN 1675-8021 http://ejournal.ukm.my/gema/issue/view/1087
institution Universiti Kebangsaan Malaysia
building Tun Sri Lanang Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Kebangsaan Malaysia
content_source UKM Journal Article Repository
url_provider http://journalarticle.ukm.my/
language English
description The extraction of Arabic nested noun compound is significant for several research areas such as sentiment analysis, text summarization, word categorization, grammar checker, and machine translation. Much research has studied the extraction of Arabic noun compound using linguistic approaches, statistical methods, or a hybrid of both. A wide range of the existing approaches concentrate on the extraction of the bi-gram or tri-gram noun compound. Nonetheless, extracting a 4-gram or 5-gram nested noun compound is a challenging task due to the morphological, orthographic, syntactic and semantic variations. Many features have an important effect on the efficiency of extracting a noun compound such as unit-hood, contextual information, and term-hood. Hence, there is a need to improve the effectiveness of the Arabic nested noun compound extraction. Thus, this paper proposes a hybrid linguistic approach and a statistical method with a view to enhance the extraction of the Arabic nested noun compound. A number of pre-processing phases are presented, including transformation, tokenization, and normalisation. The linguistic approaches that have been used in this study consist of a part-of-speech tagging and the named entities pattern, whereas the proposed statistical methods that have been used in this study consist of the NC-value, NTC-value, NLC-value, and the combination of these association measures. The proposed methods have demonstrated that the combined association measures have outperformed the NLC-value, NTC-value, and NC-value in terms of nested noun compound extraction by achieving 90%, 88%, 87%, and 81% for bigram, trigram, 4-gram, and 5-gram, respectively.
format Article
author Nazlia Omar,
Qasem Al-Tashi,
spellingShingle Nazlia Omar,
Qasem Al-Tashi,
Arabic nested noun compound extraction based on linguistic features and statistical measures
author_facet Nazlia Omar,
Qasem Al-Tashi,
author_sort Nazlia Omar,
title Arabic nested noun compound extraction based on linguistic features and statistical measures
title_short Arabic nested noun compound extraction based on linguistic features and statistical measures
title_full Arabic nested noun compound extraction based on linguistic features and statistical measures
title_fullStr Arabic nested noun compound extraction based on linguistic features and statistical measures
title_full_unstemmed Arabic nested noun compound extraction based on linguistic features and statistical measures
title_sort arabic nested noun compound extraction based on linguistic features and statistical measures
publisher Penerbit Universiti Kebangsaan Malaysia
publishDate 2018
url http://journalarticle.ukm.my/13773/1/25313-76332-1-PB.pdf
http://journalarticle.ukm.my/13773/
http://ejournal.ukm.my/gema/issue/view/1087
_version_ 1654961128299560960
score 13.211869

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