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Improving LSTM CRFs using character-based compositions for Korean named entity recognition

Computer speech & language, v.54, 2019년, pp.106 - 121  

Na, Seung-Hoon (Department of Computer Science, Chonbuk National University, South Korea) ,  Kim, Hyun (Department of Creative IT Engineering, Pohang University of Science and Technology (POSTECH), South Korea) ,  Min, Jinwoo (Department of Computer Science, Chonbuk National University, South Korea) ,  Kim, Kangil (Corresponding author.)

Abstract AI-Helper 아이콘AI-Helper

Abstract Standard approaches to named entity recognition (NER) are based on sequential labeling methods, such as conditional random fields (CRFs), which label each word in a sentence and extract entities from them that correspond to named entities. With the extensive deployment of deep learning met...

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