An enhanced malay named entity recognition using clustering and classification approach for crime textual data analysis

Named Entity Recognition (NER) is one of the tasks undertaken in the information extraction. NER is used for extracting and classifying words or entities that belong to the proper noun category in text data such as the person's name, location, organization, date, etc. As seen in today's ge...

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Main Author:	Salleh, Muhammad Sharilazlan
Format:	Thesis
Language:	English English
Published:	2018
Subjects:	Q Science (General) QA76 Computer software
Online Access:	http://eprints.utem.edu.my/id/eprint/23326/1/An%20Enhanced%20Malay%20Named%20Entity%20Recognition%20Using%20Clustering%20and%20Classification%20Approach%20For%20Crime%20Textual%20Data%20Analysis.pdf http://eprints.utem.edu.my/id/eprint/23326/2/An%20enhanced%20malay%20named%20entity%20recognition%20using%20clustering%20and%20classification%20approach%20for%20crime%20textual%20data%20analysis.pdf
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id	my-utem-ep.23326
record_format	uketd_dc
institution	Universiti Teknikal Malaysia Melaka
collection	UTeM Repository
language	English English
topic	Q Science (General) QA76 Computer software
spellingShingle	Q Science (General) QA76 Computer software Salleh, Muhammad Sharilazlan An enhanced malay named entity recognition using clustering and classification approach for crime textual data analysis
description	Named Entity Recognition (NER) is one of the tasks undertaken in the information extraction. NER is used for extracting and classifying words or entities that belong to the proper noun category in text data such as the person's name, location, organization, date, etc. As seen in today's generation, social media such as web pages, blogs, Facebook, Twitter, Instagram and online newspapers are among the major contributors to information extraction. These resources contain various types of unstructured data such as text. However, the amount of works done to process this type of data is limited for Malay Named Entity Recognition (MNER). The deficiency on Malay textual analytic has led to difficulties in extracting information for decision making. This research aims to present a Malay Named Entity Recognition technique that focuses on crime data analysis in the Malay language that extracted from Polis Diraja Malaysia (PDRM) news web page. This Malay Named Entity Recognition (MNER) technique is proposed by using multi-staged of clustering and classification methods. The methods are Fuzzy C-Means and K-Nearest Neighbors Algorithm. The methods involve multi-layer features extraction to recognize entities such as person name, location, organization, date and crime type. This multi-staged technique is obtained 95.24% accuracy in the process of recognizing named entities for text analysis, particularly in Malay. The proposed technique can improve the accuracy performance on named entity recognition of crime data based on the suitability selected features for the Malay language.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Salleh, Muhammad Sharilazlan
author_facet	Salleh, Muhammad Sharilazlan
author_sort	Salleh, Muhammad Sharilazlan
title	An enhanced malay named entity recognition using clustering and classification approach for crime textual data analysis
title_short	An enhanced malay named entity recognition using clustering and classification approach for crime textual data analysis
title_full	An enhanced malay named entity recognition using clustering and classification approach for crime textual data analysis
title_fullStr	An enhanced malay named entity recognition using clustering and classification approach for crime textual data analysis
title_full_unstemmed	An enhanced malay named entity recognition using clustering and classification approach for crime textual data analysis
title_sort	enhanced malay named entity recognition using clustering and classification approach for crime textual data analysis
granting_institution	Universiti Teknikal Malaysia Melaka
granting_department	Faculty of Information and Communication Technology
publishDate	2018
url	http://eprints.utem.edu.my/id/eprint/23326/1/An%20Enhanced%20Malay%20Named%20Entity%20Recognition%20Using%20Clustering%20and%20Classification%20Approach%20For%20Crime%20Textual%20Data%20Analysis.pdf http://eprints.utem.edu.my/id/eprint/23326/2/An%20enhanced%20malay%20named%20entity%20recognition%20using%20clustering%20and%20classification%20approach%20for%20crime%20textual%20data%20analysis.pdf
_version_	1747834035446480896
spelling	my-utem-ep.233262022-04-20T12:25:25Z An enhanced malay named entity recognition using clustering and classification approach for crime textual data analysis 2018 Salleh, Muhammad Sharilazlan Q Science (General) QA76 Computer software Named Entity Recognition (NER) is one of the tasks undertaken in the information extraction. NER is used for extracting and classifying words or entities that belong to the proper noun category in text data such as the person's name, location, organization, date, etc. As seen in today's generation, social media such as web pages, blogs, Facebook, Twitter, Instagram and online newspapers are among the major contributors to information extraction. These resources contain various types of unstructured data such as text. However, the amount of works done to process this type of data is limited for Malay Named Entity Recognition (MNER). The deficiency on Malay textual analytic has led to difficulties in extracting information for decision making. This research aims to present a Malay Named Entity Recognition technique that focuses on crime data analysis in the Malay language that extracted from Polis Diraja Malaysia (PDRM) news web page. This Malay Named Entity Recognition (MNER) technique is proposed by using multi-staged of clustering and classification methods. The methods are Fuzzy C-Means and K-Nearest Neighbors Algorithm. The methods involve multi-layer features extraction to recognize entities such as person name, location, organization, date and crime type. This multi-staged technique is obtained 95.24% accuracy in the process of recognizing named entities for text analysis, particularly in Malay. The proposed technique can improve the accuracy performance on named entity recognition of crime data based on the suitability selected features for the Malay language. 2018 Thesis http://eprints.utem.edu.my/id/eprint/23326/ http://eprints.utem.edu.my/id/eprint/23326/1/An%20Enhanced%20Malay%20Named%20Entity%20Recognition%20Using%20Clustering%20and%20Classification%20Approach%20For%20Crime%20Textual%20Data%20Analysis.pdf text en public http://eprints.utem.edu.my/id/eprint/23326/2/An%20enhanced%20malay%20named%20entity%20recognition%20using%20clustering%20and%20classification%20approach%20for%20crime%20textual%20data%20analysis.pdf text en validuser https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=112736 mphil masters Universiti Teknikal Malaysia Melaka Faculty of Information and Communication Technology 1. Abacha, A.B., and Zweigenbaum, P., 2011. 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An enhanced malay named entity recognition using clustering and classification approach for crime textual data analysis

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