A Discourse-Level Named Entity Recognition and Relation Extraction Dataset for Chinese Literature Text
This addresses a data scarcity problem for researchers working on Chinese literature NLP, but it is incremental as it primarily provides a new dataset rather than a novel method.
The authors tackled the lack of tagging sets for Named Entity Recognition and Relation Extraction in Chinese literature text by building a discourse-level dataset from hundreds of articles, and experimental results demonstrated its usefulness and provided baselines for further research.
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