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

5 Citations (Scopus)

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

Bibliographical note

Funding Information:
Funding: This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2021R1A6A1A03045425). Additionally, it was also supported by the MSIT (Ministry of Science and ICT), Korea, under the ICT Creative Consilience program (IITP-2021-2020-0-01819) supervised by the IITP (Institute for Information & communications Technology Planning & Evaluation).

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Keywords

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

ASJC Scopus subject areas

  • General Materials Science
  • Instrumentation
  • General Engineering
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes

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