An Enhanced Malay Named Entity Recognition using Combination Approach for Crime Textual Data Analysis
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2018, Vol 9, Issue 9
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.
Authors and Affiliations
Siti Azirah Asmai, Muhammad Sharilazlan Salleh, Halizah Basiron, Sabrina Ahmad
Keywords
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- EP ID EP394077
- DOI 10.14569/IJACSA.2018.090960
- Views 107
- Downloads 0
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