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.