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

TL;DR

This paper introduces a new discourse-level dataset for Chinese literature text to improve named entity recognition and relation extraction, addressing previous data scarcity and inconsistency issues.

Contribution

It presents a high-quality Chinese literature dataset with novel tagging methods and baseline experiments, advancing research in discourse-level NER and RE tasks.

Findings

The dataset improves NER and RE performance on Chinese literature texts.

Proposed tagging methods effectively address data inconsistency.

Baseline models demonstrate the dataset's usefulness for future research.

Abstract

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