- Volume 8 Issue 12
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Bi-directional LSTM-CNN-CRF for Korean Named Entity Recognition System with Feature Augmentation
자질 보강과 양방향 LSTM-CNN-CRF 기반의 한국어 개체명 인식 모델
- Lee, DongYub (Department of Computer Science and Engineering, Korea University) ;
- Yu, Wonhee (Department of Computer Science and Engineering, Korea University) ;
- Lim, HeuiSeok (Department of Computer Science and Engineering, Korea University)
- Received : 2017.10.24
- Accepted : 2017.12.20
- Published : 2017.12.28
The Named Entity Recognition system is a system that recognizes words or phrases with object names such as personal name (PS), place name (LC), and group name (OG) in the document as corresponding object names. Traditional approaches to named entity recognition include statistical-based models that learn models based on hand-crafted features. Recently, it has been proposed to construct the qualities expressing the sentence using models such as deep-learning based Recurrent Neural Networks (RNN) and long-short term memory (LSTM) to solve the problem of sequence labeling. In this research, to improve the performance of the Korean named entity recognition system, we used a hand-crafted feature, part-of-speech tagging information, and pre-built lexicon information to augment features for representing sentence. Experimental results show that the proposed method improves the performance of Korean named entity recognition system. The results of this study are presented through github for future collaborative research with researchers studying Korean Natural Language Processing (NLP) and named entity recognition system.
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