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

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

doi:10.3390/app112311472

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 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.

Highlights

IntroductionThe importance of research on automatic information extraction is recently increasing [1] with the advent of massive unstructured documents
The proposed K-EPICV method demonstrates a significant increase of 35.97%p for weighted-F1, on average, in comparison to nonK-EPIC
We demonstrate that K-EPICS achieves the best performances in mBERT, KLUE-BERT, KoBERT, and KorBERT, among the other methods in all metrics

Summary

Introduction

The importance of research on automatic information extraction is recently increasing [1] with the advent of massive unstructured documents. Information Extraction (IE), which provides the basic research for extracting structured information from unstructured resources, is considered promising research in the field of natural language processing (NLP). Among principal research in IE [2], Relation Extraction (RE) aims to predict the relation between two entities in a single sentence. RE is a significant task especially in the field of Knowledge Base Population (KBP) since it extracts structured triples. It is used for advanced research, including Question and Answering (QA) systems, Summarization, Dialogue Systems, and Information Retrieval (IR) [3]

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