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Named-Entity Recognizer (NER) for Filipino Novel Excerpts using Maximum Entropy Approach

Karen Mae L. Eboña, Orlando S. Llorca Jr., Genrev P. Perez, Jhustine M. Roldan, Iluminda Vivien R. Domingo, and Ria A. Sagum
CCIS, Polytechnic University of the Philippines, Manila, Philippines
Abstract – The Named-Entity Recognizer (NER) for Filipino Novel Excerpts using Maximum Entropy Approach is a study intended mainly for the development of a named entity recognition system specifically for handling texts written in the Filipino language. Its main purpose is to recognize the named entities present in a given text using MaxEnt. The named entities are classified into five, namely: person, place, date, organization, date, time. To measure the performance of the system, solving for the precision, recall, error rate and F-measure was used, both for every named entity and all the named entities as a whole. Novel excerpts were used as a domain for the testing of the system. The results, based on the computation of F-measure, indicated that the system is 80.53% accurate, and best in identifying entity date with 0% error rate but is unsatisfactory in recognizing place and organization, with 29.41% and 13.10% error rates respectively.

Index Terms–named-entity recognition, maximum entropy Approach, named entity, natural language processing (NLP), filipino, information extraction

Cite: Karen Mae L. Eboña, Orlando S. Llorca Jr., Genrev P. Perez, Jhustine M. Roldan, Iluminda Vivien R. Domingo, and Ria A. Sagum, "Named-Entity Recognizer (NER) for Filipino Novel Excerpts using Maximum Entropy Approach," Journal of Industrial and Intelligent Information, Vol. 1, No. 1, pp. 63-67, March 2013. doi: 10.12720/jiii.1.1.63-67
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