Abstract—A Named-Entity Recognition (NER) is part of the process in Text Mining and it is a very useful process for information extraction. This NER tool can be used to assist user in identifying and detecting entities such as person, location or organization. However, different languages may have different morphologies and thus require different NER processes. For instance, an English NER process cannot be applied in processing Malay articles due to the different morphology used in different languages. This paper proposes a Rule-Based Named-Entity Recognition algorithm for Malay articles. The proposed Malay NER is designed based on a Malay part-of-speech (POS) tagging features and contextual features that had been implemented to handle Malay articles. Based on the POS results, proper names will be identified or detected as the possible candidates for annotation. Besides that, there are some symbols and conjunctions that will also be considered in the process of identifying named-entity for Malay articles. Several manually constructed dictionaries will be used to handle three named-entities; Person, Location and Organizations. The experimental results show a reasonable output of 89.47% for the F-Measure value. The proposed Malay NER algorithm can be further improved by having more complete dictionaries and refined rules to be used in order to identify the correct Malay entities system.
Index Terms—Information extraction, Malay named entity recognition, named entity recognition, rule-based.
Rayner Alfred, Leow Chin Leong, and Chin Kim On are with the COESA, Universiti Malaysia Sabah, 88400 Kota Kinabalu, Sabah, Malaysia (e-mail: email@example.com, firstname.lastname@example.org, email@example.com).
Patricia Anthony is with the Department of Applied Computing, Faculty of Environment, Society and Design, Lincoln University, Christchurch, New Zealand (e-mail: firstname.lastname@example.org).
Cite: Rayner Alfred, Leow Chin Leong, Chin Kim On, and Patricia Anthony, "Malay Named Entity Recognition Based on Rule-Based Approach," International Journal of Machine Learning and Computing vol.4, no. 3, pp. 300-306, 2014.