mlsanityData to 7 Oct 2026

CHisIEC: An Information Extraction Corpus for Ancient Chinese History

arXiv 2403.15088 ↗First posted 22 Mar 2024cs.CLcited by 7

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Abstract (arXiv)

Natural Language Processing (NLP) plays a pivotal role in the realm of Digital Humanities (DH) and serves as the cornerstone for advancing the structural analysis of historical and cultural heritage texts. This is particularly true for the domains of named entity recognition (NER) and relation extraction (RE). In our commitment to expediting ancient history and culture, we present the ``Chinese Historical Information Extraction Corpus''(CHisIEC). CHisIEC is a meticulously curated dataset designed to develop and evaluate NER and RE tasks, offering a resource to facilitate research in the field. Spanning a remarkable historical timeline encompassing data from 13 dynasties spanning over 1830 years, CHisIEC epitomizes the extensive temporal range and text heterogeneity inherent in Chinese historical documents. The dataset encompasses four distinct entity types and twelve relation types, resulting in a meticulously labeled dataset comprising 14,194 entities and 8,609 relations. To establish the robustness and versatility of our dataset, we have undertaken comprehensive experimentation involving models of various sizes and paradigms. Additionally, we have evaluated the capabilities of Large Language Models (LLMs) in the context of tasks related to ancient Chinese history. The dataset and code are available at urlhttps://github.com/tangxuemei1995/CHisIEC.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 12 of its 32 references that are in the map. First posted on arXiv 22 Mar 2024.

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12 of 32 references.

References · 12

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  10. 2020Corpus of Chinese Dynastic Histories: Gender Analysis over Two Millenniacited by 4
  11. 2022KoCHET: a Korean Cultural Heritage corpus for Entity-related Taskscited by 4
  12. 2022Modeling Multi-Granularity Hierarchical Features for Relation Extractioncited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 4; 2026: 3.

7 papers.