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.
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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).
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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
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DOI: https://doi.org/10.1007/978-3-030-88480-2_19
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