Abstract
Previous researches on event relation classification primarily rely on lexical and syntactic features. In this paper, we use a Shallow Convolutional Neural Network (SCNN) to extract event-level and cross-event semantic features for event relation classification. On the one hand, the shallow structure alleviates the over-fitting problem caused by the lack of diverse relation samples. On the other hand, the utilization and combination of event-level and cross-event semantic information help improve relation classification. The experimental results show that our approach outperforms the state of the art.
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
Notes
References
Aharon, R.B., Szpektor, I., Dagan, I.: Generating entailment rules from framenet. In: Proceedings of ACL 2010 Conference Short Papers, pp. 241–246. Association for Computational Linguistics (2010)
Baroni, M., Dinu, G., Kruszewski, G.: Don’t count, predict! A systematic comparison of context-counting vs. context-predicting semantic vectors. In: ACL, vol. 1, pp. 238–247 (2014)
Burchardt, A., Frank, A.: Approaching textual entailment with LFG and framenet frames. In: Proceedings of 2nd PASCAL RTE Challenge Workshop. Citeseer (2006)
Chklovski, T., Pantel, P.: Global path-based refinement of noisy graphs applied to verb semantics. In: Dale, R., Wong, K.-F., Su, J., Kwong, O.Y. (eds.) IJCNLP 2005. LNCS (LNAI), vol. 3651, pp. 792–803. Springer, Heidelberg (2005)
Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., Kuksa, P.: Natural language processing (almost) from scratch. J. Mach. Learn. Res. 12, 2493–2537 (2011)
Ding, S., Hong, Y., Zhu, S., Yao, J., Zhu, Q.: Research of event relation classification based on tri-training. J. Comput. Eng. Sci. 37(12), 2345–2351 (2015)
Fillmore, C.: Frame semantics. In: Linguistics in the Morning Calm, pp. 111–137 (1982)
Fillmore, C.J., Johnson, C.R., Petruck, M.R.: Background to framenet. Int. J. Lexicogr. 16(3), 235–250 (2003)
Harris, Z.: Mathematical Structures of Language. Wiley, New York (1968)
Lin, D., Pantel, P.: Dirt@ sbt@ discovery of inference rules from text. In: Proceedings of 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 323–328. ACM (2001)
Pantel, P., Pennacchiotti, M.: Espresso: leveraging generic patterns for automatically harvesting semantic relations. In: Proceedings of 21st International Conference on Computational Linguistics and 44th Annual Meeting of Association for Computational Linguistics, pp. 113–120. Association for Computational Linguistics (2006)
Shen, D., Lapata, M.: Using semantic roles to improve question answering. In: EMNLP-CoNLL, pp. 12–21 (2007)
Socher, R., Huang, E.H., Pennin, J., Manning, C.D., Ng, A.Y.: Dynamic pooling and unfolding recursive autoencoders for paraphrase detection. In: Advances in Neural Information Processing Systems, pp. 801–809 (2011)
Yang, X., Hong, Y., Chen, Y., Wang, X., Yao, J., Zhu, Q.: Detection event relation through cross-scenario inference. J. Chin. Inf. Process. 28(5), 206–214 (2014)
Zhang, B., Su, J., Xiong, D., Lu, Y., Duan, H., Yao, J.: Shallow convolutional neural network for implicit discourse relation recognition. In: Proceedings of 2015 Conference on Empirical Methods in Natural Language Processing, pp. 2230–2235 (2015)
Acknowledgments
This research is supported by the National Natural Science Foundation of China, No.61672368, No.61373097, No.61672367, No.61272259, No.61272260, the Research Foundation of the Ministry of Education and China Mobile, MCM20150602 and the Science and Technology Plan of Jiangsu, SBK2015022101. The authors would like to thank the anonymous reviewers for their insightful comments and suggestions.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Ding, S., Hong, Y., Zhu, S., Yao, J., Zhu, Q. (2016). Combining Event-Level and Cross-Event Semantic Information for Event-Oriented Relation Classification by SCNN. In: Sun, M., Huang, X., Lin, H., Liu, Z., Liu, Y. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. NLP-NABD CCL 2016 2016. Lecture Notes in Computer Science(), vol 10035. Springer, Cham. https://doi.org/10.1007/978-3-319-47674-2_19
Download citation
DOI: https://doi.org/10.1007/978-3-319-47674-2_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-47673-5
Online ISBN: 978-3-319-47674-2
eBook Packages: Computer ScienceComputer Science (R0)