Abstract
Event argument extraction aims at identifying event arguments from texts as well as determining their respective roles in an event. Despite some neural networks applied for this task, their performance are still not satisfactory due to the following shortcomings. Syntactic information were not well explored; Event arguments were independently extracted; Pattern knowledge were not explicitly exploited. In this paper, we propose a Syntactic Distance Sensitive Neural Network model to tackle these problems. Our model first captures long-range dependencies in between event triggers and event arguments through performing graph convolution over syntactic trees, where we introduce syntactic distance to weight the importance of each word. Furthermore, we design an argument interaction module to mine argument-argument interactions according to the shortest dependency distances in between arguments. To enjoy pattern knowledge, we design a pattern-aware argument classification module to ensure the reasonability of extracted arguments. Extensive experiments have validated the superiority of the proposed model, which achieves the state-of-the-art results in terms of better F1-score on both argument identification and role classification.
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Notes
We collectively regard references to entities, value expressions and time as entity mentions
References
Han J, Wang H (2022) A meta learning approach for open information extraction. Neural Computing and Applications
Lin Y, Ji H, Huang F, Wu L (2020) A joint neural model for information extraction with global features. In: Proceedings of the 58th annual meeting of the association for computational linguistics, pp 7999–8009
Xiang W, Wang B (2019) A survey of event extraction from text. IEEE Access 7:173111–173137
Zhu L, Zheng H (2020) Biomedical event extraction with a novel combination strategy based on hybrid deep neural networks. BMC bioinformatics 21(1):1–12
Liu K, Chen Y, Liu J, Zuo X (2020) Junzhao: Extracting event and their relations from texts: A survey on recent research progress and challenges. AI Open 1:22–39
Fei H, Ren Y, Ji D (2020) A tree-based neural network model for biomedical event trigger detection. Inf Sci 512:175–185
Lu S, Li S, Xu Y, Wang K, Lan H, Guo J (2021) Event detection from text using path-aware graph convolutional network. Appl Intell, pp 1–12
Wang Z, Guo Y, Wang J (2021) Empower chinese event detection with improved atrous convolution neural networks. Neural Comput & Applic 33(11):5805–5820
Vo T (2021) Synseq4ed: a novel event-aware text representation learning for event detection. Neural Process Lett, pp 1–23
Wang X, Wang Z, Han X, Liu Z, Li J, Li P, Sun M, Zhou J, Ren X (2019) Hmeae: Hierarchical modular event argument extraction. In: Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing, pp 5777–5783
Wang X, Jia S, Han X, Liu Z, Li J, Li P, Zhou J (2020) Neural gibbs sampling for joint event argument extraction. In: Proceedings of the 1st conference of the asia-pacific chapter of the association for computational linguistics and the 10th international joint conference on natural language processing, pp 169–180
Veyseh APB, Nguyen TN, Nguyen TH (2020) Graph transformer networks with syntactic and semantic structures for event argument extraction. In: Proceedings of the 2020 conference on empirical methods in natural language processing: findings, pp 3651–3661
Li Z, Yang Z, Shen C, Xu J, Zhang Y, Xu H (2019) Integrating shortest dependency path and sentence sequence into a deep learning framework for relation extraction in clinical text. BMC medical informatics and decision making 19(1):1–8
Li Z, Sun Y, Zhu J, Tang S, Zhang C, Ma H (2021) Improve relation extraction with dual attention-guided graph convolutional networks. Neural Comput & Applic 33(6):1773–1784
Sun Q, Zhang K, Lv L, Li X, Huang K, Zhang T (2021) Joint extraction of entities and overlapping relations by improved graph convolutional networks. Appl Intell, pp 1–13
Sha L, Qian F, Chang B, Sui Z (2018) Jointly extracting event triggers and arguments by dependency-bridge rnn and tensor-based argument interaction. In: Proceedings of the 32rd AAAI Conference on Artificial Intelligence, pp 5916–5923
Liu X, Luo Z, Huang H (2018) Jointly multiple events extraction via attention-based graph information aggregation. In: Proceedings of the 2018 conference on empirical methods in Natural Language Processing, pp 1247–1256
Consortium LD (2005) Ace (automatic content extraction) english annotation guidelines for events
Hong Y, Zhang J, Ma B, Yao J, Zhou G, Zhu Q (2011) Using cross-entity inference to improve event extraction. In: Proceedings of the 49th annual meeting of the association for computational linguistics, pp 1127–1136
Chen C, Ng V (2012) Joint modeling for chinese event extraction with rich linguistic features. In: Proceedings of the 24th International Conference on Computational Linguistics, pp 529–544
Li Q, Ji H, Huang L (2013) Joint event extraction via structured prediction with global features. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, pp 73–82
Li P, Zhu Q, Zhou G (2013) Argument inference from relevant event mentions in chinese argument extraction. In: Proceedings of the 51st annual meeting of the association for computational linguistics, pp 1477–1487
Chen Y, Xu L, Liu K, Zeng D, Zhao J (2015) Event extraction via dynamic multi-pooling convolutional neural networks. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, pp 167–176
Nguyen TH, Cho K, Grishman R (2016) Joint event extraction via recurrent neural networks. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp 300–309
Li D, Huang L, Ji H, Han J (2019) Biomedical event extraction based on knowledge-driven tree-lstm. In: Proceedings of the 2019 conference of the north american chapter of the association for computational linguistics: Human Language Technologies, pp 1421–1430
Ma J, Wang S, Anubhai R, Ballesteros M, Al-Onaizan Y (2020) Resource-enhanced neural model for event argument extraction. In: Proceedings of the 2020 conference on empirical methods in natural language processing: findings, pp 3554–3559
Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I (2017) Attention is all you need. In: Proceedings of the 31st conference on neural information processing systems, pp 6000–6010
Huang L, Ji H, Cho K, Dagan I, Riedel S, Voss C (2018) Zero-shot transfer learning for event extraction. In: Proceedings of the 56th annual meeting of the association for computational linguistics, pp 2160–2170
Subburathinam A, Lu D, Ji H, May J, Chang S-F, Sil A, Voss C (2019) Cross-lingual structure transfer for relation and event extraction. In: Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing, pp 313–325
Wang Z, Wang X, Han X, Lin Y, Hou L, Liu Z, Li P, Li J, Zhou J (2021) Cleve: Contrastive pre-training for event extraction. In: Proceedings of the 59th annual meeting of the association for computational linguistics and the 11th international joint conference on natural language processing, pp 6283–6297
Ferguson J, Lockard C, Weld D, Hajishirzi H (2018) Semi-supervised event extraction with paraphrase clusters. In: Proceedings of the 2018 conference of the north american chapter of the association for computational linguistics: human language technologies, pp 359–364
Zhou Y, Chen Y, Zhao J, Wu Y, Xu J, Li J (2021) What the role is vs. what plays the role: Semi-supervised event argument extraction via dual question answering. In: Proceedings of the AAAI conference on artificial intelligence, pp 14638–14646
Zhang Y, Qi P, Manning CD (2018) Graph convolution over pruned dependency trees improves relation extraction. In: Proceedings of the 2018 conference on empirical methods in natural language processing, pp 2205–2215
Zhang S, Zhang W, Niu J (2019) Improving short-text representation in convolutional networks by dependency parsing. Knowl Inf Syst 61(1):463–484
Wang C, Wang B, Xiang W, Xu M (2019) Encoding syntactic dependency and topical information for social emotion classification. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp 881–884
Hong Y, Liu Y, Yang S, Zhang K, Hu J (2020) Joint extraction of entities and relations using graph convolution over pruned dependency trees. Neurocomputing 411:302–312
Lu Q, Zhu Z, Zhang G, Kang S, Liu P (2021) Aspect-gated graph convolutional networks for aspect-based sentiment analysis. Appl Intell 51(7):4408–4419
Doddington G, Mitchell A, Przbocki M, Ramshaw L, Strassel S, Weischedel R (2004) The automatic content extraction (ace) program-tasks, data, and evaluation. In: Proceedings of the 4th international conference on language resources and evaluation, pp 837–840
Pennington J, Socher R, Manning C. D (2014) Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, pp 1532–1543
Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929–1958
Devlin J, Chang M-W, Lee K, Toutanova K (2019) Bert: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 conference of the north american chapter of the association for computational linguistics: Human Language Technologies, pp 4171–4186
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This work is supported in part by National Natural Science Foundation of China (Grant No: 62172167).
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Dai, L., Wang, B., Xiang, W. et al. A syntactic distance sensitive neural network for event argument extraction. Appl Intell 53, 6554–6568 (2023). https://doi.org/10.1007/s10489-022-03598-x
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DOI: https://doi.org/10.1007/s10489-022-03598-x