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DOI: 10.14569/IJACSA.2018.090960
PDF

An Enhanced Malay Named Entity Recognition using Combination Approach for Crime Textual Data Analysis

Author 1: Siti Azirah Asmai
Author 2: Muhammad Sharilazlan Salleh
Author 3: Halizah Basiron
Author 4: Sabrina Ahmad

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 9 Issue 9, 2018.

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Abstract: Named Entity Recognition (NER) is one of the tasks 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 person's name, location, organization, date and others. 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 the generation of information. This paper presents an enhanced Malay Named Entity Recognition model using combination fuzzy c-means and K-Nearest Neighbours Algorithm method for crime analysis. The results showed that this combination method could improve the accuracy performance on entity recognition of crime data in Malay. The model is expected to provide a better method in the process of recognizing named entities for text analysis particularly in Malay.

Keywords: Named entity recognition; information extraction; fuzzy c-means; k-nearest neighbors; malay language; crime data

Siti Azirah Asmai, Muhammad Sharilazlan Salleh, Halizah Basiron and Sabrina Ahmad, “An Enhanced Malay Named Entity Recognition using Combination Approach for Crime Textual Data Analysis” International Journal of Advanced Computer Science and Applications(IJACSA), 9(9), 2018. http://dx.doi.org/10.14569/IJACSA.2018.090960

@article{Asmai2018,
title = {An Enhanced Malay Named Entity Recognition using Combination Approach for Crime Textual Data Analysis},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2018.090960},
url = {http://dx.doi.org/10.14569/IJACSA.2018.090960},
year = {2018},
publisher = {The Science and Information Organization},
volume = {9},
number = {9},
author = {Siti Azirah Asmai and Muhammad Sharilazlan Salleh and Halizah Basiron and Sabrina Ahmad}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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