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Myparser: a Malay text categorization toolkit using inference rule

Yaman, Maisarah (2013) Myparser: a Malay text categorization toolkit using inference rule. Masters thesis, Universiti Tun Hussein Onn Malaysia.


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Natural Language Processing (NLP) is a technique where a machine can understand human better and thereby reduce the distance between human being. NLP was implemented in MyParser, acting as a tool of parsing sentence. This research aimed to develop a sentence parser application for teachers and learners of the Malay language who faced difficulties in comprehending the grammar and phrase structure. There are three major processing steps that have been drawn which are Input Component, Parsing Engine and Output Component. Parsing engine phase involved pre-processing phase; the use of 'Tokenizer' and 'Part Of Speech' (POS). The input component is a simple sentence provided by an expert of Malay Language (Munsyi Dewan). It is categorized based on the inference rule implemented in MyParser. This study is significant in designing inference rules for Malay Language sentences, focusing on the relationship between computing language. The output was labelled according to each phrase following the Malay Context Free Grammar (CFG) rule. Thus, parsing technique is an essential component to be considered in parsing application development. Besides that, MyParser application was developed to process Malay simple sentence by categorizing them into different grammar and phrase structure. The target groups for this application are students and teachers of Malay Language subject in primary school. MyParser was evaluated using 100 training data which were agreed by a qualified expert on Malay Language of Malaysia (Munsyi Dewan). More than thousand words were stored in the database. This application was found to be able to visualize correct sentence with its labelled graphical tags. MyParser was tested by different level of teachers from primary and secondary school and Munsyi Dewan. The results proved that MyParser achieved more than 90% accuracy in constructing sentences based on its grammatical rule.

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > QA76 Computer software
Depositing User: Normajihan Abd. Rahman
Date Deposited: 10 Dec 2014 08:08
Last Modified: 10 Dec 2014 08:08
URI: http://eprints.uthm.edu.my/id/eprint/6150
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