K-EPIC: Entity-perceived context representation in Korean relation extraction

Yuna Hur, Suhyune Son, Midan Shim, Jungwoo Lim, Heuiseok Lim

Research output: Contribution to journalArticlepeer-review

Abstract

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 pretrained 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.

Original languageEnglish
Article number11472
JournalApplied Sciences (Switzerland)
Volume11
Issue number23
DOIs
Publication statusPublished - 2021 Dec 1

Keywords

  • Deep learning
  • Information extraction
  • Korean pre-trained language model
  • Relation extraction

ASJC Scopus subject areas

  • Materials Science(all)
  • Instrumentation
  • Engineering(all)
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes

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