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  2. Master Thesis / Tesis Master
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Please use this identifier to cite or link to this item: https://ptsldigital.ukm.my/jspui/handle/123456789/476359
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dc.contributor.advisorNazlia Omar, Assoc. Prof. Dr.
dc.contributor.authorMohanaed Ajmi Falih (P72244)
dc.date.accessioned2023-10-06T09:17:00Z-
dc.date.available2023-10-06T09:17:00Z-
dc.date.issued2015
dc.identifier.otherukmvital:82998
dc.identifier.urihttps://ptsldigital.ukm.my/jspui/handle/123456789/476359-
dc.descriptionGrammatical Relation (GR) can be defined as a linguistic relation established by grammar, where the linguistic relation is an association between linguistic forms or constituents. Fundamentally, GRs determine grammatical behaviour, such as the placement of a word in a clause, verb agreement and passivity behaviour.The GR of Arabic is a necessary prerequisite for many natural language processing applications, such as machine translation and information retrieval. This study focuses on GR related problems of Arabic. The main difficulty of determining grammatical relations in Arabic sentences is ambiguity. This grammatical ambiguity is caused by the large and complex nature of Arabic sentences. The main goal of this study is to develop an efficient GR extraction technique to analyse modern standard Arabic sentences and addresses these issues with an optimum solution. This research proposes a machine learning classification method to recognize subject, object and verb. In order to extract the correct subject, object and verb from sentence structure, the proposed technique enhances the basic representations of Arabic using Naive Bayes (NB), Support Vector Machines (SVM), K-Nearest Neighbour (KNN) and a combination between SVM and KNN algorithms. The system used 80 Arabic sentences as a training and test data set, with the length of each sentence ranging from 3 to 20 words. The results obtained through combination classification between SVM and KNN algorithms achieved 94.49 % recall, 93.46 % precision and 93.97 % F-measure. This result proves the viability of this approach for the GR extraction of Arabic sentences.,Certification of Master's/Doctoral Thesis" is not available
dc.language.isoeng
dc.publisherUKM, Bangi
dc.relationFaculty of Information Science and Technology / Fakulti Teknologi dan Sains Maklumat
dc.rightsUKM
dc.subjectGrammatical relation
dc.subjectArabic sentences
dc.subjectClassification
dc.subjectDissertations, Academic -- Malaysia
dc.titleArabic grammatical relation extraction based on machine learning classification
dc.typetheses
dc.format.pages111
Appears in Collections:Faculty of Information Science and Technology / Fakulti Teknologi dan Sains Maklumat

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