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http://dx.doi.org/10.3745/KIPSTB.2003.10B.2.229

HMM-based Korean Named Entity Recognition  

Hwang, Yi-Gyu (한국전자통신연구원)
Yun, Bo-Hyun (목원대학교 컴퓨터교육과)
Publication Information
The KIPS Transactions:PartB / v.10B, no.2, 2003 , pp. 229-236 More about this Journal
Abstract
Named entity recognition is the process indispensable to question answering and information extraction systems. This paper presents an HMM based named entity (m) recognition method using the construction principles of compound words. In Korean, many named entities can be decomposed into more than one word. Moreover, there are contextual relationships among nouns in an NE, and among an NE and its surrounding words. In this paper, we classify words into a word as an NE in itself, a word in an NE, and/or a word adjacent to an n, and train an HMM based on NE-related word types and parts of speech. Proposed named entity recognition (NER) system uses trigram model of HMM for considering variable length of NEs. However, the trigram model of HMM has a serious data sparseness problem. In order to solve the problem, we use multi-level back-offs. Experimental results show that our NER system can achieve an F-measure of 87.6% in the economic articles.
Keywords
Named Entity Recognition; Information Extraction; Trigram; Variable Length Named Entity; HMM;
Citations & Related Records
Times Cited By KSCI : 9  (Citation Analysis)
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