Abstract
Document-level Relation Extraction (DocRE) is the task of extracting relational facts mentioned in the entire document. Despite its popularity, there are still two major difficulties with this task: (i) How to learn more informative embeddings for entity pairs? (ii) How to capture the crucial context describing the relation between an entity pair from the document? To tackle the first challenge, we propose to encode the document with a task-specific pre-trained encoder, where three tasks are involved in pre-training. While one novel task is designed to learn the relation semantic from diverse expressions by utilizing relation-aware pre-training data, the other two tasks, Masked Language Modeling (MLM) and Mention Reference Prediction (MRP), are adopted to enhance the encoder’s capacity in text understanding and coreference capturing. For addressing the second challenge, we craft a hierarchical attention mechanism to refine the context for entity pairs, which considers the embeddings from the encoder as well as the sequential distance information of mentions in the given document. Extensive experimental study on the benchmark dataset DocRED verifies that our method achieves better performance than the baselines.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
- 2.
- 3.
- 4.
- 5.
- 6.
- 7.
- 8.
References
Christopoulou, F., Miwa, M., Ananiadou, S.: Connecting the dots: Document-level neural relation extraction with edge-oriented graphs. arXiv preprint arXiv:1909.00228 (2019)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)
Guo, Z., Zhang, Y., Lu, W.: Attention guided graph convolutional networks for relation extraction. arXiv preprint arXiv:1906.07510 (2019)
Gupta, P., Rajaram, S., Schütze, H., Runkler, T.: Neural relation extraction within and across sentence boundaries. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 6513–6520 (2019)
Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2006), vol. 2, pp. 1735–1742. IEEE (2006)
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Luan, Y., Wadden, D., He, L., Shah, A., Ostendorf, M., Hajishirzi, H.: A general framework for information extraction using dynamic span graphs. arXiv preprint arXiv:1904.03296 (2019)
Mintz, M., Bills, S., Snow, R., Jurafsky, D.: Distant supervision for relation extraction without labeled data. In: Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP, pp. 1003–1011 (2009)
Nan, G., Guo, Z., Sekulić, I., Lu, W.: Reasoning with latent structure refinement for document-level relation extraction. arXiv preprint arXiv:2005.06312 (2020)
Peng, N., Poon, H., Quirk, C., Toutanova, K., Yih, W.T.: Cross-sentence n-ary relation extraction with graph LSTMs. Trans. Assoc. Comput. Linguist. 5, 101–115 (2017)
Quirk, C., Poon, H.: Distant supervision for relation extraction beyond the sentence boundary. arXiv preprint arXiv:1609.04873 (2016)
Sahu, S.K., Christopoulou, F., Miwa, M., Ananiadou, S.: Inter-sentence relation extraction with document-level graph convolutional neural network. arXiv preprint arXiv:1906.04684 (2019)
Soares, L.B., FitzGerald, N., Ling, J., Kwiatkowski, T.: Matching the blanks: Distributional similarity for relation learning. arXiv preprint arXiv:1906.03158 (2019)
Song, L., Zhang, Y., Wang, Z., Gildea, D.: N-ary relation extraction using graph state LSTM. arXiv preprint arXiv:1808.09101 (2018)
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
Sun, H., Ma, H., Yih, W.T., Tsai, C.T., Liu, J., Chang, M.W.: Open domain question answering via semantic enrichment. In: Proceedings of the 24th International Conference on World Wide Web, pp. 1045–1055 (2015)
Tang, H., et al.: HIN: hierarchical inference network for document-level relation extraction. In: Lauw, H.W., Wong, R.C.-W., Ntoulas, A., Lim, E.-P., Ng, S.-K., Pan, S.J. (eds.) PAKDD 2020. LNCS (LNAI), vol. 12084, pp. 197–209. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-47426-3_16
Tenney, I., Das, D., Pavlick, E.: Bert rediscovers the classical nlp pipeline. arXiv preprint arXiv:1905.05950 (2019)
Vaswani, A., et al.: Attention is all you need. arXiv preprint arXiv:1706.03762 (2017)
Velikovi, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., Bengio, Y.: Graph attention networks (2017)
Verga, P., Strubell, E., McCallum, A.: Simultaneously self-attending to all mentions for full-abstract biological relation extraction. arXiv preprint arXiv:1802.10569 (2018)
Wang, H., Focke, C., Sylvester, R., Mishra, N., Wang, W.: Fine-tune bert for docred with two-step process. arXiv preprint arXiv:1909.11898 (2019)
Wang, L., Cao, Z., De Melo, G., Liu, Z.: Relation classification via multi-level attention CNNs. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1298–1307 (2016)
Weikum, G., Theobald, M.: From information to knowledge: harvesting entities and relationships from web sources. In: Proceedings of the Twenty-Ninth ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, pp. 65–76 (2010)
Xiao, C., et al.: Denoising relation extraction from document-level distant supervision. arXiv preprint arXiv:2011.03888 (2020)
Xu, B., Wang, Q., Lyu, Y., Zhu, Y., Mao, Z.: Entity structure within and throughout: Modeling mention dependencies for document-level relation extraction. arXiv preprint arXiv:2102.10249 (2021)
Yao, Y., et al.: Docred: A large-scale document-level relation extraction dataset. arXiv preprint arXiv:1906.06127 (2019)
Ye, D., et al.: Coreferential reasoning learning for language representation. arXiv preprint arXiv:2004.06870 (2020)
Zeng, S., Xu, R., Chang, B., Li, L.: Double graph based reasoning for document-level relation extraction. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
Acknowledgments
We are grateful to Heng Ye, Jiaan Wang and all reviews for their constructive comments. This work was supported by the National Key R&D Program of China (No. 2018AAA0101900), the Priority Academic Program Development of Jiangsu Higher Education Institutions, National Natural Science Foundation of China (Grant No. 62072323, 61632016, 62102276), Natural Science Foundation of Jiangsu Province (No. BK20191420).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Zou, M. et al. (2021). Document-Level Relation Extraction with Entity Enhancement and Context Refinement. In: Zhang, W., Zou, L., Maamar, Z., Chen, L. (eds) Web Information Systems Engineering – WISE 2021. WISE 2021. Lecture Notes in Computer Science(), vol 13081. Springer, Cham. https://doi.org/10.1007/978-3-030-91560-5_25
Download citation
DOI: https://doi.org/10.1007/978-3-030-91560-5_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-91559-9
Online ISBN: 978-3-030-91560-5
eBook Packages: Computer ScienceComputer Science (R0)