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

TL;DR

KoCHET is a comprehensive Korean cultural heritage corpus designed for entity recognition, relation extraction, and entity typing, facilitating research and practical applications in digital preservation of cultural documents.

Contribution

It introduces a large, expert-advised Korean cultural heritage corpus for key entity-related NLP tasks, with flexible redistribution rights for researchers.

Findings

High-quality, large-scale dataset for Korean cultural heritage entities

Enhanced practical usability demonstrated through experimental results

Provides valuable insights via statistical and linguistic analysis

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.