1. UKM Learning and Research Repository
2. Master Thesis / Tesis Master
3. Faculty of Information Science and Technology / Fakulti Teknologi dan Sains Maklumat

Title:	Arabic grammatical relation extraction based on machine learning classification
Authors:	Mohanaed Ajmi Falih (P72244)
Supervisor:	Nazlia Omar, Assoc. Prof. Dr.
Keywords:	Grammatical relation Arabic sentences Classification Dissertations, Academic -- Malaysia
Issue Date:	2015
Description:	Grammatical 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
Pages:	111
Publisher:	UKM, Bangi
Appears in Collections:	Faculty of Information Science and Technology / Fakulti Teknologi dan Sains Maklumat

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.