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

arXiv: https://arxiv.org/abs/2209.00367 · HTML: https://mlsanity.com/paper/2209.00367/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 18 references that are in the map.
- First posted on arXiv 1 Sep 2022.

## Builds on (most cited first, 7 of 7)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [How multilingual is Multilingual BERT?](https://mlsanity.com/paper/1906.01502/) (2019)
- [K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters](https://mlsanity.com/paper/2002.01808/) (2020)
- [Ultra-Fine Entity Typing](https://mlsanity.com/paper/1807.04905/) (2018)
- [Context-Dependent Fine-Grained Entity Type Tagging](https://mlsanity.com/paper/1412.1820/) (2014)
- [Corpus of Chinese Dynastic Histories: Gender Analysis over Two Millennia](https://mlsanity.com/paper/2005.08793/) (2020)

## Built on by (most cited first, 4 of 4)

- [CHisIEC: An Information Extraction Corpus for Ancient Chinese History](https://mlsanity.com/paper/2403.15088/) (2024)
- [Team ACK at SemEval-2025 Task 2: Beyond Word-for-Word Machine Translation for English-Korean Pairs](https://mlsanity.com/paper/2504.20451/) (2025)
- [From National Curricula to Cultural Awareness: Constructing Open-Ended Culture-Specific Question Answering Dataset](https://mlsanity.com/paper/2601.04632/) (2026)
- [CultureMINE: Datasets and Methods for Improving the Cultural Capabilities of NLP Systems](https://mlsanity.com/paper/2609.22494/) (2026)

## Abstract (arXiv)

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.