K-EPIC: Entity-Perceived Context Representation in Korean Relation Extraction

Applied Sciences

Heuiseok Lim¹  Suhyune Son¹  Jungwoo Lim¹  Yuna Hur¹  Midan Shim² 

[1] Department of Computer Science and Engineering, Korea University, 145, Anam-ro, Seongbuk-gu, Seoul 02841, Korea;Human-Inspired AI Research, Korea University, 145, Anam-ro, Seongbuk-gu, Seoul 02841, Korea;

关键词: information extraction;    relation extraction;    Korean pre-trained language model;    deep learning;   

DOI  :  10.3390/app112311472

来源: DOAJ

【 摘 要 】

Relation Extraction (RE) aims to predict the correct relation between two entities from the given sentence. To obtain the proper relation in Relation Extraction (RE), it is significant to comprehend the precise meaning of the two entities as well as the context of the sentence. In contrast to the RE research in English, Korean-based RE studies focusing on the entities and preserving Korean linguistic properties rarely exist. Therefore, we propose K-EPIC (Entity-Perceived Context representation in Korean) to ensure enhanced capability for understanding the meaning of entities along with considering linguistic characteristics in Korean. We present the experimental results on the BERT-Ko-RE and KLUE-RE datasets with four different types of K-EPIC methods, utilizing entity position tokens. To compare the ability of understanding entities and context of Korean pre-trained language models, we analyze HanBERT, KLUE-BERT, KoBERT, KorBERT, KoELECTRA, and multilingual-BERT (mBERT). The experimental results demonstrate that the F1 score increases significantly with our K-EPIC and that the performance of the language models trained with the Korean corpus outperforms the baseline.

【 授权许可】

Unknown