Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Analyzing the effectiveness and applicability of co-training
Proceedings of the ninth international conference on Information and knowledge management
Modified Kneser-Ney Smoothing of n-gram Models
Modified Kneser-Ney Smoothing of n-gram Models
Nymble: a high-performance learning name-finder
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Named entity recognition using an HMM-based chunk tagger
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Automatic rule learning exploiting morphological features for named entity recognition in Turkish
Journal of Information Science
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Named entity recognition is important in sophisticated information service system such as Question Answering and Text Mining since most of the answer type and text mining unit depend on the named entity type. Therefore we focus on named entity recognition model in Korean. Korean named entity recognition is difficult since each word of named entity has not specific features such as the capitalizing feature of English. It has high dependence on the large amounts of hand-labeled data and the named entity dictionary, even though these are tedious and expensive to create. In this paper, we devise HMM based named entity recognizer to consider various context models. Furthermore, we consider weakly supervised learning technique, CoTraining, to combine labeled data and unlabeled data.