# A Discourse-Level Named Entity Recognition and Relation Extraction Dataset for Chinese Literature Text

arXiv: https://arxiv.org/abs/1711.07010 · HTML: https://mlsanity.com/paper/1711.07010/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Nov 2017.

## Built on by (most cited first, 13 of 13)

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- [Multitask Pre-training of Modular Prompt for Chinese Few-Shot Learning](https://mlsanity.com/paper/2210.07565/) (2022)
- [Beyond Boundaries: Learning a Universal Entity Taxonomy across Datasets and Languages for Open Named Entity Recognition](https://mlsanity.com/paper/2406.11192/) (2024)
- [Leveraging Encoder-only Large Language Models for Mobile App Review Feature Extraction](https://mlsanity.com/paper/2408.01063/) (2024)
- [Dynamic Multi-View Fusion Mechanism For Chinese Relation Extraction](https://mlsanity.com/paper/2303.05082/) (2023)
- [Razmecheno: Named Entity Recognition from Digital Archive of Diaries "Prozhito"](https://mlsanity.com/paper/2201.09997/) (2022)
- [Rethinking Relation Classification with Graph Meaning Representations](https://mlsanity.com/paper/2310.09772/) (2023)
- [A Corpus for Named Entity Recognition in Chinese Novels with Multi-genres](https://mlsanity.com/paper/2311.15509/) (2023)
- [A Two Dimensional Feature Engineering Method for Relation Extraction](https://mlsanity.com/paper/2404.04959/) (2024)

## Abstract (arXiv)

Named Entity Recognition and Relation Extraction for Chinese literature text is regarded as the highly difficult problem, partially because of the lack of tagging sets. In this paper, we build a discourse-level dataset from hundreds of Chinese literature articles for improving this task. To build a high quality dataset, we propose two tagging methods to solve the problem of data inconsistency, including a heuristic tagging method and a machine auxiliary tagging method. Based on this corpus, we also introduce several widely used models to conduct experiments. Experimental results not only show the usefulness of the proposed dataset, but also provide baselines for further research. The dataset is available at https://github.com/lancopku/Chinese-Literature-NER-RE-Dataset