Named entity recognition of South China Sea conflicts
Online news articles not only provide us with useful and reliable information and reports, it also eases information extraction and gathering for research purposes especially in Natural Language Processing (NLP) and machine learning (ML). The topics regarding the South China Sea have been popular la...
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| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2020
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/93748/1/NurRafeeqkhaSulaiman2020_NamedEntityRecognitionofSouthChina.pdf http://eprints.utm.my/id/eprint/93748/ http://dx.doi.org/10.1088/1757-899X/884/1/012057 |
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| Summary: | Online news articles not only provide us with useful and reliable information and reports, it also eases information extraction and gathering for research purposes especially in Natural Language Processing (NLP) and machine learning (ML). The topics regarding the South China Sea have been popular lately due to the rise of conflicts between several countries claim on the islands in the sea. Gathering data through Internet and online sources proves to be easy, but to process a huge amount of data and to identify only useful information is no longer possible. Because of that, relevant information and the classification of news articles in relation to the conflicts need to be done. In this paper, a model is proposed to use NER that search for and classifies important information regarding to the conflicts. In order to do that, a combination of POS and NER are needed to extract meaningful information from the news. This study also aims to classify conflict related news by using Conditional Random Field (CRF) algorithm as classification method by training and testing the data. |
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