SIAT: Document-level Event Extraction via Spatiality-Augmented Interaction Model with Adaptive Thresholding
Authors: 
Zekun Tao, Changjian Wang, Zhiliang Tian, Kele Xu, Yong Guo, Shanshan Li, Yanru Bai, Da XieAuthors Info & Claims
Zekun Tao, Changjian Wang, Zhiliang Tian, Kele Xu, Yong Guo, Shanshan Li, Yanru Bai, Da XieAuthors Info & ClaimsArticle No.: 147, Pages 1 - 21
Abstract
Document-level event extraction endeavors to automatically extract structural events from a given document. Many existing approaches focus on modeling entity interactions and decoding these interactions into events, assigning each entity as an event argument. However, these approaches encounter two primary limitations: they exclusively capture semantic dependencies to model entity interactions, overlooking the indication of the spatial distribution features of entities; they decode interactions imprecisely with a hard binary-classification boundary, potentially failing to calibrate micro differences in interactions. To overcome these limitations, we introduce a novel approach termed the Spatiality-augmented Interaction Model with Adaptive Thresholding (SIAT). Our method addresses the first limitation by calculating the relative position encoding of entities to represent spatial interaction features. These features are then integrated with multi-granularity semantic interactions, enhancing the modeling of entity interactions for each entity pair. Furthermore, we introduce an adaptive event decoding mechanism, which establishes a more flexible decision boundary for different entity interactions. Additionally, an adaptive loss function for threshold learning is designed to further refine the model. Experimental results demonstrate that our proposed method achieves competitive performance compared to state-of-the-art methods on two public event extraction datasets while maintaining considerable training efficiency.
References
[1]
Jawad Ali, Nasir Saleem, Sami Bourouis, Eatedal Alabdulkreem, Hela El Mannai, and Sami Dhahbi. 2024. Spatio-temporal features representation using recurrent capsules for monaural speech enhancement. IEEE Access (2024).
[2]
Jari Björne and Tapio Salakoski. 2018. Biomedical event extraction using convolutional neural networks and dependency parsing. In Proceedings of the BioNLP 2018 Workshop. 98–108. DOI:
[3]
Antoine Bosselut, Ronan Le Bras, and Yejin Choi. 2021. Dynamic neuro-symbolic knowledge graph construction for zero-shot commonsense question answering. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35. 4923–4931. DOI:
[4]
Jordan Boyd-Graber and Benjamin Börschinger. 2020. What question answering can learn from Trivia Nerds. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 7422–7435. DOI:
[5]
Qingqing Cao, Harsh Trivedi, Aruna Balasubramanian, and Niranjan Balasubramanian. 2020. DeFormer: Decomposing pre-trained transformers for faster question answering. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 4487–4497. DOI:
[6]
Yubo Chen, Shulin Liu, Shizhu He, Kang Liu, and Jun Zhao. 2016. Event extraction via bidirectional long short-term memory tensor neural networks. In Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data: 15th China National Conference, CCL 2016, and 4th International Symposium, NLP-NABD 2016, Yantai, China, October 15–16, 2016, Proceedings 4. Springer, 190–203. DOI:
[7]
Yubo Chen, Shulin Liu, Xiang Zhang, Kang Liu, and Jun Zhao. 2017. Automatically labeled data generation for large scale event extraction. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, Vancouver, Canada, 409–419. DOI:
[8]
Yubo Chen, Liheng Xu, Kang Liu, Daojian Zeng, and Jun Zhao. 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 (Volume 1: Long Papers). Association for Computational Linguistics, Beijing, China, 167–176. DOI:
[9]
Ziyang Chen, Wenbin Shi, Xianchao Zhang, and Chien-Hung Yeh. 2024. Temporal self-attentional and adaptive graph convolutional mixed model for sleep staging. IEEE Sensors Journal (2024). DOI:
[10]
Kyunghyun Cho, Bart van Merriënboer, Çağlar Gulçehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. Learning phrase representations using RNN encoder–decoder for statistical machine translation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP’14). 1724–1734. DOI:
[11]
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In NAACL. 4171–4186. https://aclanthology.org/N19-1423/
[12]
Xinya Du, Sha Li, and Heng Ji. 2022. Dynamic global memory for document-level argument extraction. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, Dublin, Ireland, 5264–5275. DOI:
[13]
Seth Ebner, Patrick Xia, Ryan Culkin, Kyle Rawlins, and Benjamin Van Durme. 2020. Multi-sentence argument linking. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Online, 8057–8077. DOI:
[14]
Li Gao, Jia Wu, Zhi Qiao, Chuan Zhou, Hong Yang, and Yue Hu. 2016. Collaborative social group influence for event recommendation. In Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. 1941–1944. DOI:
[15]
Yanbu Guo, Dongming Zhou, Pu Li, Chaoyang Li, and Jinde Cao. 2022. Context-aware poly (a) signal prediction model via deep spatial–temporal neural networks. IEEE Transactions on Neural Networks and Learning Systems (2022). DOI:
[16]
Cuiyun Han, Jinchuan Zhang, Xinyu Li, Guojin Xu, Weihua Peng, and Zengfeng Zeng. 2022. DuEE-Fin: A large-scale dataset for document-level event extraction. In CCF International Conference on Natural Language Processing and Chinese Computing. Springer, 172–183. DOI:
[17]
Guang Han, Kang Wu, Fanyu Zeng, Jixin Liu, and Sam Kwong. 2023. Dual-stream adaptive convergent low-light image enhancement network based on frequency perception. IEEE Transactions on Computational Imaging (2023). DOI:
[18]
Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural Computation 9, 8 (1997), 1735–1780. DOI:
[19]
Guanhua Huang, Runxin Xu, Ying Zeng, Jiaze Chen, Zhouwang Yang, and E. Weinan. 2023. An iteratively parallel generation method with the pre-filling strategy for document-level event extraction. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 10834–10852. DOI:
[20]
Yusheng Huang and Weijia Jia. 2021. Exploring sentence community for document-level event extraction. In Findings of the Association for Computational Linguistics: EMNLP 2021. Association for Computational Linguistics, Punta Cana, Dominican Republic, 340–351. DOI:
[21]
Diederik P. Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7–9, 2015, Conference Track Proceedings, Yoshua Bengio and Yann LeCun (Eds.). http://arxiv.org/abs/1412.6980
[22]
Thomas N. Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016). https://arxiv.org/abs/1609.02907
[23]
John Lafferty, Andrew McCallum, and Fernando C. N. Pereira. 2001. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In ICML. 282–289. https://repository.upenn.edu/handle/20.500.14332/6188
[24]
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. 1998. Gradient-based learning applied to document recognition. Proc. IEEE 86, 11 (1998), 2278–2324. DOI:
[25]
Sha Li, Heng Ji, and Jiawei Han. 2021. Document-level event argument extraction by conditional generation. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, Online, 894–908. DOI:
[26]
Weihua Li, Yanbu Guo, Bingyi Wang, and Bei Yang. 2023. Learning spatiotemporal embedding with gated convolutional recurrent networks for translation initiation site prediction. Pattern Recognition 136 (2023), 109234. DOI:
[27]
Yuan Liang, Zhuoxuan Jiang, Di Yin, and Bo Ren. 2022. RAAT: Relation-augmented attention transformer for relation modeling in document-level event extraction. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, Seattle, Washington, USA, 4985–4997. DOI:
[28]
Chun-Yi Liu, Chuan Zhou, Jia Wu, Hongtao Xie, Yue Hu, and Li Guo. 2017. CPMF: A collective pairwise matrix factorization model for upcoming event recommendation. In 2017 International Joint Conference on Neural Networks (IJCNN’17). IEEE, 1532–1539. DOI:
[29]
Xiao Liu, Heyan Huang, Ge Shi, and Bo Wang. 2022. Dynamic prefix-tuning for generative template-based event extraction. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, Dublin, Ireland, 5216–5228. DOI:
[30]
Xiao Liu, Zhunchen Luo, and Heyan Huang. 2018. Jointly multiple events extraction via attention-based graph information aggregation. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Brussels, Belgium, 1247–1256. DOI:
[31]
Yuhan Liu, Neng Gao, Yifei Zhang, and Zhe Kong. 2024. Enhancing document-level event extraction via structure-aware heterogeneous graph with multi-granularity subsentences. In ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP’24). IEEE, 12657–12661. DOI:
[32]
Yubo Ma, Zehao Wang, Yixin Cao, Mukai Li, Meiqi Chen, Kun Wang, and Jing Shao. 2022. Prompt for extraction? PAIE: Prompting argument interaction for event argument extraction. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 6759–6774. https://aclanthology.org/2022.acl-long.466/
[33]
Bonan Min, Benjamin Rozonoyer, Haoling Qiu, Alexander Zamanian, Nianwen Xue, and Jessica MacBride. 2021. ExcavatorCovid: Extracting events and relations from text corpora for temporal and causal analysis for COVID-19. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. 63–71. DOI:
[34]
Mike Mintz, Steven Bills, Rion Snow, and Daniel Jurafsky. 2009. Distant supervision for relation extraction without labeled data. In Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP. Association for Computational Linguistics, Suntec, Singapore, 1003–1011. https://aclanthology.org/P09-1113
[35]
Thien Nguyen and Ralph Grishman. 2018. Graph convolutional networks with argument-aware pooling for event detection. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 32. DOI:
[36]
Thien Huu Nguyen, Kyunghyun Cho, and Ralph Grishman. 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. Association for Computational Linguistics, San Diego, California, USA, 300–309. DOI:
[37]
Yubing Ren, Yanan Cao, Fang Fang, Ping Guo, Zheng Lin, Wei Ma, and Yi Liu. 2022. CLIO: Role-interactive multi-event head attention network for document-level event extraction. In Proceedings of the 29th International Conference on Computational Linguistics. International Committee on Computational Linguistics, Gyeongju, Republic of Korea, 2504–2514. https://aclanthology.org/2022.coling-1.221
[38]
Lei Sha, Feng Qian, Baobao Chang, and Zhifang Sui. 2018. Jointly extracting event triggers and arguments by dependency-bridge RNN and tensor-based argument interaction. Proceedings of the AAAI Conference on Artificial Intelligence 32, 1 (Apr.2018). DOI:
[39]
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in Neural Information Processing Systems 30 (2017). https://arxiv.org/abs/1706.03762
[40]
Lingzhi Wang, Shafiq Joty, Wei Gao, Xingshan Zeng, and Kam-Fai Wong. 2024. Improving conversational recommender system via contextual and time-aware modeling with less domain-specific knowledge. IEEE Transactions on Knowledge and Data Engineering (2024). DOI:
[41]
Runxin Xu, Tianyu Liu, Lei Li, and Baobao Chang. 2021. Document-level event extraction via heterogeneous graph-based interaction model with a tracker. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Association for Computational Linguistics, Online, 3533–3546. DOI:
[42]
Runxin Xu, Peiyi Wang, Tianyu Liu, Shuang Zeng, Baobao Chang, and Zhifang Sui. 2022. A two-stream AMR-enhanced model for document-level event argument extraction. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 5025–5036. https://aclanthology.org/2022.naacl-main.370/
[43]
Haoran Yan, Xiaolong Jin, Xiangbin Meng, Jiafeng Guo, and Xueqi Cheng. 2019. Event detection with multi-order graph convolution and aggregated attention. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP’19). Association for Computational Linguistics, Hong Kong, China, 5766–5770. DOI:
[44]
Hang Yang, Yubo Chen, Kang Liu, Yang Xiao, and Jun Zhao. 2018. DCFEE: A document-level Chinese financial event extraction system based on automatically labeled training data. In Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, 50–55. DOI:
[45]
Hang Yang, Yubo Chen, Kang Liu, Jun Zhao, Zuyu Zhao, and Weijian Sun. 2022. Multi-turn and multi-granularity reader for document-level event extraction. ACM Transactions on Asian and Low-Resource Language Information Processing 22, 2 (2022), 1–16. DOI:
[46]
Hang Yang, Dianbo Sui, Yubo Chen, Kang Liu, Jun Zhao, and Taifeng Wang. 2021. Document-level event extraction via parallel prediction networks. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Association for Computational Linguistics, Online, 6298–6308. DOI:
[47]
Sen Yang, Dawei Feng, Linbo Qiao, Zhigang Kan, and Dongsheng Li. 2019. Exploring pre-trained language models for event extraction and generation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 5284–5294. DOI:
[48]
Shaoliang Yang, Dongming Zhou, Jinde Cao, and Yanbu Guo. 2023. LightingNet: An integrated learning method for low-light image enhancement. IEEE Transactions on Computational Imaging 9 (2023), 29–42. DOI:
[49]
Daojian Zeng, Kang Liu, Yubo Chen, and Jun Zhao. 2015. Distant supervision for relation extraction via piecewise convolutional neural networks. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Lisbon, Portugal, 1753–1762. DOI:
[50]
Shun Zheng, Wei Cao, Wei Xu, and Jiang Bian. 2019. Doc2EDAG: An end-to-end document-level framework for Chinese financial 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 (EMNLP-IJCNLP’19). Association for Computational Linguistics, Hong Kong, China, 337–346. DOI:
[51]
Tong Zhu, Xiaoye Qu, Wenliang Chen, Zhefeng Wang, Baoxing Huai, Nicholas Yuan, and Min Zhang. 2022. Efficient document-level event extraction via pseudo-trigger-aware pruned complete graph. In Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, Lud De Raedt (Ed.). International Joint Conferences on Artificial Intelligence Organization, 4552–4558. DOI:Main Track.
Index Terms
- SIAT: Document-level Event Extraction via Spatiality-Augmented Interaction Model with Adaptive Thresholding
Recommendations
EADRE: Event-type Aware Dynamic Representation of Entities in Document-level Event Extraction
Document-level event extraction aims to identify event types and arguments from one document. However, existing methods fail to consider semantic distinctions between multiple mentions of one entity and ignore dynamic representation of entities across ...
Token-Event-Role Structure-Based Multi-Channel Document-Level Event Extraction
Document-level event extraction is a long-standing challenging information retrieval problem involving a sequence of sub-tasks: entity extraction, event type judgment, and event type-specific multi-event extraction. However, addressing the problem as ...
Explainable document-level event extraction via back-tracing to sentence-level event clues
AbstractDocument-level event extraction (DEE) aims to extract events directly from a document. Current state-of-the-art methods follow the entity-centric framework, capturing sentence-level event clues into entity instance representations for ...
Graphical abstractDisplay Omitted
Highlights- An explainable document-level event extraction framework ExpDEE is proposed.
- ...
Comments
Information & Contributors
Information
Published In
October 2024
189 pages
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 24 October 2024
Online AM: 07 October 2024
Accepted: 14 September 2024
Revised: 11 July 2024
Received: 01 March 2024
Published in TALLIP Volume 23, Issue 10
Check for updates
Author Tags
Qualifiers
- Research-article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 127Total Downloads
- Downloads (Last 12 months)127
- Downloads (Last 6 weeks)36
Reflects downloads up to 02 Mar 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in