Thai Named Entity Extraction by incorporating Maximum Entropy Model with Simple Heuristic Information
نویسندگان
چکیده
The role of Named entity (NE) extraction is very important in many NLP tasks, such as information extraction, etc. In Thai, the problems of NE extraction are much more difficult due to the characteristics of Thai language, that are lack of orthographical information to signal NEs, and no boundary indicator between words. In this paper, we present Thai NE extraction system by using Maximum Entropy model, with heuristic information and dictionary. Our system is divided into three steps. The first step is to identify the boundary of candidate NE that composes of many words by using heuristic rules, dictionary and statistic of word cooccurrence. The second step is NE extraction by using Maximum Entropy model. The final step is to extract the undiscovered NE by matching the extracted NEs against the rest of document. On Thai political news test data, the evaluation of the system shows that the Fmeasures of person, location, and organization names are 90.44%, 82.16% and 89.87% respectively.
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