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Moradi, Hamed, Ahmadi, Farid, Feizi-Derakhshi, Mohammad-Reza. (1394). A hybrid approach for Persian Named Entity Recognition. , (), -. doi: 10.22099/ijsts.2015.3181
Hamed Moradi; Farid Ahmadi; Mohammad-Reza Feizi-Derakhshi. "A hybrid approach for Persian Named Entity Recognition". , , , 1394, -. doi: 10.22099/ijsts.2015.3181
Moradi, Hamed, Ahmadi, Farid, Feizi-Derakhshi, Mohammad-Reza. (1394). 'A hybrid approach for Persian Named Entity Recognition', , (), pp. -. doi: 10.22099/ijsts.2015.3181
Moradi, Hamed, Ahmadi, Farid, Feizi-Derakhshi, Mohammad-Reza. A hybrid approach for Persian Named Entity Recognition. , 1394; (): -. doi: 10.22099/ijsts.2015.3181

A hybrid approach for Persian Named Entity Recognition

Iranian Journal of Science
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 22 شهریور 1394
نوع مقاله: Regular Paper
شناسه دیجیتال (DOI): 10.22099/ijsts.2015.3181
نویسندگان
Hamed Moradi* 1؛ Farid Ahmadi1؛ Mohammad-Reza Feizi-Derakhshi2
1Department of Information Technology, Urmia University of Technology, Urmia, Iran
2Department of Computer Science, University of Tabriz, Tabriz, Iran
چکیده
Named Entity Recognition (NER) is an information extraction subtask that attempts to recognize and categorize named entities in unstructured text into predefined categories such as the names of people, organizations, and locations. Recently, machine learning approaches, such as Hidden Markov Model (HMM) as well as hybrid methods, are frequently used to solve Name Entity Recognition. Since the absence of publicly available data sets for NER in Persian, as our knowledge does not exist any machine learning base Persian NER system. Because of HMM innate weaknesses, in this paper, we have used both Hidden Markov Model and rule-based method to recognize named entities in Persian texts. The combination of rule-based method and machine learning method results in a high accurate recognition. The proposed system in it's machine learning section uses from HMM and Viterbi algorithms; and in it's rule-based section employs a set of lexical resources and pattern bases for the recognition of named entities including the names of people, locations and organizations. During this study, we annotate our own training and testing data sets to use in the related phases. Our hybrid approach performs on Persian language with 89.73% precision, 82.44% recall, and 85.93% F-measure using an annotated test corpus including 32,606 tokens.
کلیدواژه‌ها
Information Retrieval؛ Text Processing؛ Natural Language Processing؛ Named Entity Recognition؛ Hidden Markov Model
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