Korean Semantic Role Labeling with BERT 


Vol. 47,  No. 11, pp. 1021-1026, Nov.  2020
10.5626/JOK.2020.47.11.1021


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  Abstract

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.


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  Cite this article

[IEEE Style]

J. Bae, C. Lee, S. Lim, H. Kim, "Korean Semantic Role Labeling with BERT," Journal of KIISE, JOK, vol. 47, no. 11, pp. 1021-1026, 2020. DOI: 10.5626/JOK.2020.47.11.1021.


[ACM Style]

Jangseong Bae, Changki Lee, Soojong Lim, and Hyunki Kim. 2020. Korean Semantic Role Labeling with BERT. Journal of KIISE, JOK, 47, 11, (2020), 1021-1026. DOI: 10.5626/JOK.2020.47.11.1021.


[KCI Style]

배장성, 이창기, 임수종, 김현기, "BERT를 이용한 한국어 의미역 결정," 한국정보과학회 논문지, 제47권, 제11호, 1021~1026쪽, 2020. DOI: 10.5626/JOK.2020.47.11.1021.


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