Named Entity Tagging for Korean Using DL-CoTrain Algorithm

摘要

Our approach to solve the problem of Korean named entity classification adopted a co-training method called DL-CoTrain. We use only a part-of-speech tagger and a simple noun phrase chunker instead of a full parser to extract the contextual features of a named entity. We will discuss the linguistic features in Korean which are valuable for named entity classification and experimentally show how large a labeled corpus and which unlabeled corpus is necessary for the better performance and portability of a named entity classifier. With only about a quarter of the labeled corpus, our method can compete with its supervised counterpart.