TY  - JOUR
T1  - Korean Semantic Role Labeling with BERT
AU  - Bae, Jangseong 
AU  - Lee, Changki 
AU  - Lim, Soojong 
AU  - Kim, Hyunki 
JO  - Journal of KIISE, JOK
PY  - 2020
DA  - 2020/1/14
DO  - 10.5626/JOK.2020.47.11.1021
KW  - Bidirectional Encoder Representations from Transformers
KW  - semantic role labeling
KW  - machine learning
KW  - language model
AB  - Semantic role labeling is an application of natural language processing to identify relationships such as "who, what, how and why" with in a sentence. The semantic role labeling study mainly uses machine learning algorithms and the end-to-end method that excludes feature information. Recently, a language model called BERT (Bidirectional Encoder Representations from Transformers) has emerged in the natural language processing field, performing better than the state-of- the-art models in the natural language processing field. The performance of the semantic role labeling study using the end-to-end method is mainly influenced by the structure of the machine learning model or the pre-trained language model. Thus, in this paper, we apply BERT to the Korean semantic role labeling to improve the Korean semantic role labeling performance. As a result, the performance of the Korean semantic role labeling model using BERT is 85.77%, which is better than the existing Korean semantic role labeling model.