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kochet-a-korean-cultural-heritage-corpus-for-entity-related-tasks's Introduction

KoCHET: A Korean Cultural Heritage corpus for Entity-related Tasks [Accepted at COLING 2022]

Paper Conference Demo

We propose πŸ‡°πŸ‡· KoCHET dataset v 1.0 πŸ‡°πŸ‡· Korean cultural heritage corpus for entity-related tasks, named entity recognition (NER), relation extraction (RE), and entity typing (ET).

This research was conducted within the framework of the project "Development of Intelligent Curation and Service Platform-based Digital Asset for Immersive Cultural Heritage" presented in the South Korean Ministry of Culture.


πŸ”Ž Download

KoCHET is uploaded here


πŸ“‚ Folder Description

  • KoCHET Directory Link:
KoCHET - a Korean cultural heritage corpus
  β”œβ”€ Named Entity Recognition (NER):
                                β”œβ”€ ner_{train,dev,test}.json
                                β”œβ”€ coarse_list.txt
  β”œβ”€ Relation Extraction (RE):
                          β”œβ”€ re_{train,dev,test}.json
                          β”œβ”€ relation_list.txt
  β”œβ”€ Entity Typing (NER):
                    β”œβ”€ et_{train,dev,test}.json
                    β”œβ”€ fine_grained_list.txt
  • License: KoCHET is intended for non-commercial research purpose only.

πŸ“‘ Dataset Statistics

Task Train Dev Test
NER # of examples 89,884 11,245 11,233
# of entities 393,076 32,003 32,153
RE # of examples 31,012 3,876 3,877
# of relations 64,080 8,031 7,831
ET # of examples 90,558 11,320 11,320
# of mentions 266,209 33,226 33,395

πŸ“‘ Baseline

NER RE ET
Model Entity F1 macro Entity F1 micro Char F1 macro Char F1 micro F1 macro F1 micro F1 macro F1 micro
mBERT 59.81 58.99 71.80 90.44 80.85 89.94 91.64 91.60
XLM-RoBERTa-base 76.57 79.67 82.69 94.77 80.29 89.79 91.13 91.00
KLUE-BERT-base 39.31 33.40 55.63 81.06 82.44 90.77 93.08 93.02
KLUE-RoBERTa-base 38.92 33.48 55.47 81.08 82.42 90.78 92.80 92.84

Citation

πŸ‘ Please cite our paper in any published work that uses any of these resources. πŸ‘

@inproceedings{kim-etal-2022-kochet,
    title = "{K}o{CHET}: A {K}orean Cultural Heritage Corpus for Entity-related Tasks",
    author = "Kim, Gyeongmin  and
      Kim, Jinsung  and
      Son, Junyoung  and
      Lim, Heuiseok",
    booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
    month = oct,
    year = "2022",
    address = "Gyeongju, Republic of Korea",
    publisher = "International Committee on Computational Linguistics",
    url = "https://aclanthology.org/2022.coling-1.308",
    pages = "3496--3505",
    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.",
}

πŸ“§ If you have any questions / comment related to this work, please do not hesitate to contact the authors: Gyeongmin Kim [email protected], Jinsung Kim [email protected], Junyoung Son [email protected].

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Sungguk Cha avatar  avatar Jeong-Minju avatar Hee Seung Yun avatar Jiseong avatar gyunggyung avatar 김병쀀 avatar μ†‘μ˜μˆ™ avatar huang1298 avatar Eunbin Park avatar DongChan avatar Junyoung Son avatar

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