Abstract
Information extraction is an important foundation for knowledge graph construction, as well as many natural language understanding applications. Similar to many other artificial intelligence tasks, high quality annotated datasets are essential to train a high-performance information extraction system. Existing datasets, however, are mostly built for English. To promote research in Chinese information extraction and evaluate the performance of related systems, we build a large-scale high-quality dataset, named DuIE, and make it publicly available. We design an efficient coarse-to-fine procedure including candidate generation and crowdsourcing annotation, in order to achieve high data quality at a large data scale. DuIE contains 210,000 sentences and 450,000 instances covering 49 types of commonly used relations, reflecting the real-world scenario. We also hosted an open competition based on DuIE, which attracted 1,896 participants. The competition results demonstrated the potential of this dataset in promoting information extraction research.
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Li, S. et al. (2019). DuIE: A Large-Scale Chinese Dataset for Information Extraction. In: Tang, J., Kan, MY., Zhao, D., Li, S., Zan, H. (eds) Natural Language Processing and Chinese Computing. NLPCC 2019. Lecture Notes in Computer Science(), vol 11839. Springer, Cham. https://doi.org/10.1007/978-3-030-32236-6_72
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DOI: https://doi.org/10.1007/978-3-030-32236-6_72
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