Abstract—The goal of NER is to detect named entities in an open document. Many techniques are used to solve the NER problem. Most Malay Named Entity Recognition uses rule based and gazette to tag the names for each entity. In this paper, we tested online news articles using Stanford NER and Illinois NER to measure the capability of these NER tools to detect Malay Named Entities. The results are computed using the CoNLL evaluation metric. Stanford NER tends to produce higher results on F1 and Precision compared to Illinois NER. In the future, more NER systems will be evaluated to measure the compatibility of the tools to recognize Malay Named Entities.
Index Terms—Malay named entity recognition, named entity, Malay.
S. Sulaiman, R. Abdul Wahid, and S. Sarkawi are with the Sultan Idris Education University, 35900 Tg Malim, Perak, Malaysia (e-mail: suliana@fskik.upsi.edu.my, rohaizah@fskik.upsi.edu.my, suliana@fskik.upsi.edu.my).
N.Omar is with Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia (e-mail: no@ukm.edu.my).
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Cite:S. Sulaiman, R. Abdul Wahid, S. Sarkawi, and N. Omar, "Using Stanford NER and Illinois NER to Detect Malay Named Entity Recognition," International Journal of Computer Theory and Engineering vol. 9, no. 2, pp. 147-150, 2017.
