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Erschienen in:
An Improved Class-Center Method for Text Classification Using Dependencies and WordNet Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

Event Temporal Relation Classification Based on Graph Convolutional Networks

verfasst von : Qianwen Dai, Fang Kong, Qianying Dai

Erschienen in: Natural Language Processing and Chinese Computing

Verlag: Springer International Publishing

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Abstract

Classifying temporal relations between events is an important step of understanding natural language, and a significant subsequent study of event extraction. With the development of deep learning, various neural network frameworks have been applied to the task of event temporal relation classification. However, current studies only consider semantic information in local contexts of two events and ignore the syntactic structure information. To solve this problem, this paper proposes a neural architecture combining LSTM and GCN. This method can automatically extract features from word sequences and dependency syntax. A series of experiments on the Timebank-Dense corpus also show the superiority of the model presented in this paper.

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Vorheriges Kapitel A New Algorithm for Component Decomposition and Type Recognition of Tibetan Syllable
Nächstes Kapitel Event Factuality Detection in Discourse
Literatur
1.
Zheng, X., Li, P.F., Zhu, Q.M., et al.: Annotation and classification of temporal relation between chinese events. Comput. Sci. 42(7), 276–279 (2015)
2.
Mani, I., Verhagen, M., Wellner, B., et al.: Machine learning of temporal relations. In: Proceedings of the 21st International Conference on Computational Linguistics and the 44th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, pp. 753–760 (2006)
3.
Chambers, N., Wang, S., Jurafsky, D.: Classifying temporal relations between events. In: Meeting of the ACL on Interactive Poster and Demonstration Sessions. Association for Computational Linguistics, pp. 173–176 (2007)
4.
Miller, G.A.: WordNet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995)CrossRef
5.
Chklovski, T., PVerbocean, P.: Mining the Web for fine-grained semantic verb relations. In: Conference on Empirical Methods in Natural Language Processing (2004)
6.
Cheng, Y., Asahara, M., Matsumoto, Y.: NAIST. Japan: temporal relation identification using dependency parsed tree. In: Proceedings of the 4th International Workshop on Semantic Evaluations. Association for Computational Linguistics, pp. 245–248 (2007)
7.
Bethard, S., Martin, J.H.: CU-TMP: temporal relation classification using syntactic and semantic features. In: Proceedings of the 4th International Workshop on Semantic Evaluations. Association for Computational Linguistics, pp. 129–132 (2007)
8.
Kolomiyets, O., Bethard, S., Moens, M.F.: Extracting narrative timelines as temporal dependency structures. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers-Volume 1. Association for Computational Linguistics, pp. 88–97 (2012)
9.
Xu, Y., Mou, L., Li, G., et al.: Classifying relations via long short term memory networks along shortest dependency paths. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 1785–1794 (2015)
10.
Cheng, F., Miyao, Y.: Classifying temporal relations by bidirectional LSTM over dependency paths. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 1–6 (2017)
11.
Choubey, P.K., Huang, R.A.: Sequential model for classifying temporal relations between intra-sentence events. arXiv preprint arXiv:​1707.​07343 (2017)
12.
Ning, Q., Wu, H., Peng, H., et al.: Improving temporal relation extraction with a globally acquired statistical resource. arXiv preprint arXiv:​1804.​06020 (2018)
13.
Ning, Q., Feng, Z., Wu, H., et al.: Joint reasoning for temporal and causal relations. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 2278–2288 (2018)
14.
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:​1609.​02907 (2016)
15.
Marcheggiani, D., Titov, I.: Encoding sentences with graph convolutional networks for semantic role labeling. arXiv preprint arXiv:​1703.​04826 (2017)
16.
Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: Proceedings of EMNLP (2014)
17.
Chambers, N., Cassidy, T., McDowell, B., et al.: Dense event ordering with a multi-pass architecture. Trans. Assoc. Comput. Linguist. 2, 273–284 (2014)CrossRef
18.
Paramita, M., Tonelli, S.: On the contribution of word embeddings to temporal relation classification. In: 26th International Conference on Computational Linguistics (Coling 2016): Technical Papers, pp. 2818–2828 (2016)
Metadaten
Titel
Event Temporal Relation Classification Based on Graph Convolutional Networks
verfasst von
Qianwen Dai
Fang Kong
Qianying Dai
Copyright-Jahr
2019
Buch
Natural Language Processing and Chinese Computing

Print ISBN: 978-3-030-32235-9

Electronic ISBN: 978-3-030-32236-6

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-030-32236-6_35

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