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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Zeng, D., Liu, K., Lai, S., et al.: Relation classification via convolutional deep neural network (2014)
Jiang, X., Wang, Q., Li, P., et al.: Relation extraction with multi-instance multi-label convolutional neural networks. In: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pp. 1471–1480 (2016)
Zeng, X., He, S., Liu, K., et al.: Large scaled relation extraction with reinforcement learning. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)
Miwa, M., Bansal, M.: End-to-end relation extraction using LSTMs on sequences and tree structures. arXiv preprint: arXiv:1601.00770 (2016)
Dai, D., Xiao, X., Lyu, Y., Dou, S., She, Q., Wang, H.: Joint extraction of entities and overlapping relations using position-attentive sequence labeling. In: AAAI (2019)
Takanobu, R., Zhang, T., Liu, J., et al.: A hierarchical framework for relation extraction with reinforcement learning. arXiv preprint: arXiv:1811.03925 (2018)
Zheng, S., Wang, F., Bao, H., Hao, Y., Zhou, P., Xu, B.: Joint extraction of entities and relations based on a novel tagging scheme. In: ACL (2017)
Zeng, X., Zeng, D., He, S., et al.: Extracting relational facts by an end-to-end neural model with copy mechanism. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics. Long Papers, vol. 1, pp. 148–163 (2018)
Riedel, S., Yao, L., McCallum, A.: Modeling relations and their mentions without labeled text. In: Balcázar, J.L., Bonchi, F., Gionis, A., Sebag, M. (eds.) ECML PKDD 2010, Part III. LNCS (LNAI), vol. 6323, pp. 148–163. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15939-8_10
Hendrickx, I., Kim, S.N., Kozareva, Z., et al.: SemEval-2010 task 8: Multi-way classification of semantic relations between pairs of nominals. In: Proceedings of the Workshop on Semantic Evaluations: Recent Achievements and Future Directions, pp. 94–99. Association for Computational Linguistics (2009)
Han, X., Zhu, H., Yu, P., et al.: FewRel: a large-scale supervised few-shot relation classification dataset with state-of-the-art evaluation. arXiv preprint: arXiv:1810.10147 (2018)
Devlin, J., et al.: Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint: arXiv:1810.04805 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-030-32236-6_72
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32235-9
Online ISBN: 978-3-030-32236-6
eBook Packages: Computer ScienceComputer Science (R0)