Abstract
Most previous studies on event coreference resolution usually focused on measuring the similarity between two event sentences. However, a sentence may contain more than one event and the redundant event information will interfere with the calculation of event similarity. To address the above issue, this paper proposes an event coreference resolution framework based on event sentence compression mechanism, which used an AutoEncoder-based model to compress the extracted event sentences based on the event triggers. Meanwhile, the information interaction between the compressed sentences and their original event sentences is used to supplement the missing important information in the compressed sentences. Experimental results on both KBP 2016 and KBP 2017 datasets show that our proposed model outperforms several state-of-the-art baselines.
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
References
Bagga, A.: Evaluation of coreferences and coreference resolution systems. In: LREC, pp. 563–572 (1998)
Bejan, C.A., Harabagiu, S.M.: Unsupervised event coreference resolution with rich linguistic features. In: ACL, pp. 1412–1422 (2010)
Eirew, A., Cattan, A., Dagan, I.: WEC: deriving a large-scale cross-document event coreference dataset from Wikipedia. In: NAACL-HLT, pp. 2498–2510 (2021)
Fang, J., Li, P.: Data augmentation with reinforcement learning for document-level event coreference resolution. In: NLPCC (1), pp. 751–763 (2020)
Huang, Y.J., Lu, J., Kurohashi, S., Ng, V.: Improving event coreference resolution by learning argument compatibility from unlabeled data. In: NAACL-HLT (1), pp. 785–795 (2019)
Krause, S., Xu, F., Uszkoreit, H., Weissenborn, D.: Event linking with sentential features from convolutional neural networks. In: CoNLL, pp. 239–249 (2016)
Lin, T., Goyal, P., Girshick, R.B., He, K., Dollár, P.: Focal loss for dense object detection. IEEE Trans. Pattern Anal. Mach. Intell. 42(2), 318–327 (2020)
Liu, S., Chen, Y., Liu, K., Zhao, J.: Exploiting argument information to improve event detection via supervised attention mechanisms. In: ACL (1), pp. 1789–1798 (2017)
Lu, J., Ng, V.: Joint learning for event coreference resolution. In: ACL (1), pp. 90–101 (2017)
Luo, X.: On coreference resolution performance metrics. In: HLT/EMNLP, pp. 25–32 (2005)
Malireddy, C., Maniar, T., Shrivastava, M.: SCAR: sentence compression using autoencoders for reconstruction. In: ACL (student), pp. 88–94 (2020)
Mitamura, T., Liu, Z., Hovy, E.H.: Overview of TAC KBP 2015 event nugget track. In: TAC (2015)
Peng, H., Song, Y., Roth, D.: Event detection and co-reference with minimal supervision. In: EMNLP, pp. 392–402 (2016)
Recasens, M., Hovy, E.H.: BLANC: implementing the rand index for coreference evaluation. Nat. Lang. Eng. 17(4), 485–510 (2011)
Vilain, M.B., Burger, J.D., Aberdeen, J.S., Connolly, D., Hirschman, L.: A model-theoretic coreference scoring scheme. In: MUC, pp. 45–52 (1995)
Walker, C., Strassel, S., Medero, J., Maeda, K.: ACE 2005 multilingual training corpus. Prog. Theor. Phys. Suppl. 110(110), 261–276 (2006)
Wayne, C.L.: Topic detection & tracking: a case study in corpues creation & evaluation methodologies. In: LREC, pp. 111–116 (1998)
Weissenborn, D., Wiese, G., Seiffe, L.: Making neural QA as simple as possible but not simpler. In: CoNLL, pp. 271–280 (2017)
Acknowledgments
The authors would like to thank the three anonymous reviewers for their comments on this paper. This research was supported by the National Natural Science Foundation of China (No. 61836007, 61772354 and 61773276.), and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).
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
Chen, X., Xu, S., Li, P., Zhu, Q. (2021). Employing Sentence Compression to Improve Event Coreference Resolution. In: Wang, L., Feng, Y., Hong, Y., He, R. (eds) Natural Language Processing and Chinese Computing. NLPCC 2021. Lecture Notes in Computer Science(), vol 13028. Springer, Cham. https://doi.org/10.1007/978-3-030-88480-2_19
Download citation
DOI: https://doi.org/10.1007/978-3-030-88480-2_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-88479-6
Online ISBN: 978-3-030-88480-2
eBook Packages: Computer ScienceComputer Science (R0)
