Nymble: a high-performance learning name-finder
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Named entity chunking techniques in supervised learning for Japanese named entity recognition
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
Named entity extraction based on a maximum entropy model and transformation rules
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Formal Grammar for Hispanic Named Entities Analysis
CICLing '09 Proceedings of the 10th International Conference on Computational Linguistics and Intelligent Text Processing
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This paper presents the HMM (Hidden Markov Model) based named entity recognition method for information extraction. In Korean language, named entities have the distinct characteristics unlike other languages. Many named entities can be decomposed into more than one word. Moreover, there are contextual relationship between named entities and their surrounding words. There are many internal and external evidences in named entities. To overcome data sparseness problem, we used multi-level back-off methods. The experimental result shows the F-measure of 87.6% in the economic article domain.