KOCHET: a Korean Cultural Heritage corpus for Entity-related Tasks

Gyeongmin Kim, Jinsung Kim, Junyoung Son, Heuiseok Lim

Research output: Contribution to journalConference articlepeer-review

Abstract

As digitized traditional cultural heritage documents have rapidly increased, resulting in an increased need for preservation and management, practical recognition of entities and typification of their classes has become essential. To achieve this, we propose KOCHET - a Korean cultural heritage corpus for the typical entity-related tasks, i.e., named entity recognition (NER), relation extraction (RE), and entity typing (ET). Advised by cultural heritage experts based on the data construction guidelines of government-affiliated organizations, KOCHET consists of respectively 112,362, 38,765, 113,198 examples for NER, RE, and ET tasks, covering all entity types related to Korean cultural heritage. Moreover, unlike the existing public corpora, modified redistribution can be allowed both domestic and foreign researchers. Our experimental results make the practical usability of KOCHET more valuable in terms of cultural heritage. We also provide practical insights of KOCHET in terms of statistical and linguistic analysis. Our corpus is freely available at https://github.com/Gyeongmin47/KoCHET.

Original languageEnglish
Pages (from-to)3496-3505
Number of pages10
JournalProceedings - International Conference on Computational Linguistics, COLING
Volume29
Issue number1
Publication statusPublished - 2022
Event29th International Conference on Computational Linguistics, COLING 2022 - Gyeongju, Korea, Republic of
Duration: 2022 Oct 122022 Oct 17

Bibliographical note

Funding Information:
This research is supported by Ministry of Culture, Sports and Tourism and Korea Creative Content Agency(Project Number: R2020040045), MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2018-0-01405) supervised by the IITP(Institute for Information & Communications Technology Planning & Evaluation), and Institute of Information & communications Technology Planning & Evaluation(IITP) grant funded by the Korea government(MSIT) (No. 2020-0-00368, A Neural-Symbolic Model for Knowledge Acquisition and Inference Techniques).

Publisher Copyright:
© 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Theoretical Computer Science

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