A large-scale dataset for korean document-level relation extraction from encyclopedia texts

Document-level relation extraction (RE) aims to predict the relational facts between two given entities from a document. Unlike widespread research on document-level RE in English, Korean document-level RE research is still at the very beginning due to the absence of a dataset. To accelerate the stu...

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Published inApplied intelligence (Dordrecht, Netherlands) Vol. 54; no. 17-18; pp. 8681 - 8701
Main Authors Son, Suhyune, Lim, Jungwoo, Koo, Seonmin, Kim, Jinsung, Kim, Younghoon, Lim, Youngsik, Hyun, Dongseok, Lim, Heuiseok
Format Journal Article
LanguageEnglish
Published New York Springer US 01.09.2024
Springer Nature B.V
Subjects
Annotations
Artificial Intelligence
Computer Science
Cultural heritage
Datasets
Documents
Encyclopedias
Korean language
Language
Machines
Manufacturing
Mechanical Engineering
Processes
Qualitative analysis
Statistical analysis
Subject specialists
Document-level Relation Extraction
Information Extraction
Korean Relation Extraction
Natural Language Processing
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Summary:Document-level relation extraction (RE) aims to predict the relational facts between two given entities from a document. Unlike widespread research on document-level RE in English, Korean document-level RE research is still at the very beginning due to the absence of a dataset. To accelerate the studies, we present TREK ( T oward Document-Level R elation E xtraction in K orean) dataset constructed from Korean encyclopedia documents written by the domain experts. We provide detailed statistical analyses for our large-scale dataset and human evaluation results suggest the assured quality of TREK . Also, we introduce the document-level RE model that considers the named entity-type while considering the Korean language’s properties. In the experiments, we demonstrate that our proposed model outperforms the baselines and conduct qualitative analysis.
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ISSN:0924-669X
1573-7497
DOI:10.1007/s10489-024-05605-9