TY  - JOUR
T1  - Korean Semantic Role Labeling using Stacked Bidirectional LSTM-CRFs
AU  - Bae, Jangseong 
AU  - Lee, Changki 
JO  - Journal of KIISE, JOK
PY  - 2017
DA  - 2017/1/14
DO  - 
KW  - End-to-end SRL
KW  - Semantic Role Labeling
KW  - Deep-learning
AB  - Syntactic information represents the dependency relation between predicates and arguments, and it is helpful for improving the performance of Semantic Role Labeling systems. However, syntax analysis can cause computational overhead and inherit incorrect syntactic information. To solve this problem, we exclude syntactic information and use only morpheme information to construct Semantic Role Labeling systems. In this study, we propose an end-to-end SRL system that only uses morpheme information with Stacked Bidirectional LSTM-CRFs model by extending the LSTM RNN that is suitable for sequence labeling problem. Our experimental results show that our proposed model has better performance, as compare to other models.