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CNN 기반 관계 추출 모델의 성능 향상을 위한 다중-어의 단어 임베딩 적용
남상하(Sangha Nam),한기종(Kijong Han),김은경(Eun-kyung Kim),권성구(Sunggoo Kwon),정유성(Yoosung Jung),최기선(Key-Sun Choi) Korean Institute of Information Scientists and Eng 2018 정보과학회논문지 Vol.45 No.8
The relation extraction task is to classify a relation between two entities in an input sentence and is important in natural language processing and knowledge extraction. Many studies have designed a relation extraction model using a distant supervision method. Recently the deep-learning based relation extraction model became mainstream such as CNN or RNN. However, the existing studies do not solve the homograph problem of word embedding used as an input of the model. Therefore, model learning proceeds with a single embedding value of homogeneous terms having different meanings; that is, the relation extraction model is learned without grasping the meaning of a word accurately. In this paper, we propose a relation extraction model using multi-sense word embedding. In order to learn multi-sense word embedding, we used a word sense disambiguation module based on the CoreNet concept, and the relation extraction model used CNN and PCNN models to learn key words in sentences.