{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T11:29:52Z","timestamp":1764588592607,"version":"3.41.0"},"reference-count":51,"publisher":"Association for Computing Machinery (ACM)","issue":"10","license":[{"start":{"date-parts":[[2024,10,24]],"date-time":"2024-10-24T00:00:00Z","timestamp":1729728000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Asian Low-Resour. Lang. Inf. Process."],"published-print":{"date-parts":[[2024,10,31]]},"abstract":"<jats:p>\n            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\n            <jats:bold>S<\/jats:bold>\n            patiality-augmented\n            <jats:bold>I<\/jats:bold>\n            nteraction Model with\n            <jats:bold>A<\/jats:bold>\n            daptive\n            <jats:bold>T<\/jats:bold>\n            hresholding (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.\n          <\/jats:p>","DOI":"10.1145\/3698261","type":"journal-article","created":{"date-parts":[[2024,10,7]],"date-time":"2024-10-07T11:06:28Z","timestamp":1728299188000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["SIAT: Document-level Event Extraction via Spatiality-Augmented Interaction Model with Adaptive Thresholding"],"prefix":"10.1145","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-5477-0222","authenticated-orcid":false,"given":"Zekun","family":"Tao","sequence":"first","affiliation":[{"name":"National University of Defense Technology, Changsha, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1160-0365","authenticated-orcid":false,"given":"Changjian","family":"Wang","sequence":"additional","affiliation":[{"name":"National University of Defense Technology, Changsha, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8906-5198","authenticated-orcid":false,"given":"Zhiliang","family":"Tian","sequence":"additional","affiliation":[{"name":"National University of Defense Technology, Changsha, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5997-5169","authenticated-orcid":false,"given":"Kele","family":"Xu","sequence":"additional","affiliation":[{"name":"National University of Defense Technology, Changsha, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5903-5302","authenticated-orcid":false,"given":"Yong","family":"Guo","sequence":"additional","affiliation":[{"name":"National University of Defense Technology, Changsha, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0798-974X","authenticated-orcid":false,"given":"Shanshan","family":"Li","sequence":"additional","affiliation":[{"name":"National University of Defense Technology, Changsha, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3959-7898","authenticated-orcid":false,"given":"Yanru","family":"Bai","sequence":"additional","affiliation":[{"name":"Tianjin University, Tianjin, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-9513-7217","authenticated-orcid":false,"given":"Da","family":"Xie","sequence":"additional","affiliation":[{"name":"National University of Defense Technology, Changsha, China"}]}],"member":"320","published-online":{"date-parts":[[2024,10,24]]},"reference":[{"key":"e_1_3_2_2_2","article-title":"Spatio-temporal features representation using recurrent capsules for monaural speech enhancement","author":"Ali Jawad","year":"2024","unstructured":"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).","journal-title":"IEEE Access"},{"key":"e_1_3_2_3_2","doi-asserted-by":"crossref","first-page":"98","DOI":"10.18653\/v1\/W18-2311","volume-title":"Proceedings of the BioNLP 2018 Workshop","author":"Bj\u00f6rne Jari","year":"2018","unstructured":"Jari Bj\u00f6rne and Tapio Salakoski. 2018. Biomedical event extraction using convolutional neural networks and dependency parsing. In Proceedings of the BioNLP 2018 Workshop. 98\u2013108. DOI:10.18653\/v1\/W18-2311"},{"key":"e_1_3_2_4_2","first-page":"4923","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"35","author":"Bosselut Antoine","year":"2021","unstructured":"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\u20134931. DOI:10.1609\/aaai.v35i6.16625"},{"key":"e_1_3_2_5_2","doi-asserted-by":"crossref","first-page":"7422","DOI":"10.18653\/v1\/2020.acl-main.662","volume-title":"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics","author":"Boyd-Graber Jordan","year":"2020","unstructured":"Jordan Boyd-Graber and Benjamin B\u00f6rschinger. 2020. What question answering can learn from Trivia Nerds. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 7422\u20137435. DOI:10.18653\/v1\/2020.acl-main.662"},{"key":"e_1_3_2_6_2","first-page":"4487","volume-title":"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics","author":"Cao Qingqing","year":"2020","unstructured":"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\u20134497. DOI:10.18653\/v1\/2020.acl-main.411"},{"key":"e_1_3_2_7_2","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1007\/978-3-319-47674-2_17","volume-title":"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\u201316, 2016, Proceedings 4","author":"Chen Yubo","year":"2016","unstructured":"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\u201316, 2016, Proceedings 4. Springer, 190\u2013203. DOI:10.1007\/978-3-319-47674-2_17"},{"key":"e_1_3_2_8_2","doi-asserted-by":"crossref","first-page":"409","DOI":"10.18653\/v1\/P17-1038","volume-title":"Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","author":"Chen Yubo","year":"2017","unstructured":"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\u2013419. DOI:10.18653\/v1\/P17-1038"},{"key":"e_1_3_2_9_2","first-page":"167","volume-title":"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)","author":"Chen Yubo","year":"2015","unstructured":"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\u2013176. DOI:10.3115\/v1\/P15-1017"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2024.3371456"},{"key":"e_1_3_2_11_2","first-page":"1724","volume-title":"Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP\u201914)","author":"Cho Kyunghyun","year":"2014","unstructured":"Kyunghyun Cho, Bart van Merri\u00ebnboer, \u00c7a\u011flar Gul\u00e7ehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. Learning phrase representations using RNN encoder\u2013decoder for statistical machine translation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP\u201914). 1724\u20131734. DOI:10.3115\/v1\/D14-1179"},{"key":"e_1_3_2_12_2","first-page":"4171","volume-title":"NAACL","author":"Devlin Jacob","year":"2019","unstructured":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In NAACL. 4171\u20134186. https:\/\/aclanthology.org\/N19-1423\/"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.acl-long.361"},{"key":"e_1_3_2_14_2","doi-asserted-by":"crossref","first-page":"8057","DOI":"10.18653\/v1\/2020.acl-main.718","volume-title":"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics","author":"Ebner Seth","year":"2020","unstructured":"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\u20138077. DOI:10.18653\/v1\/2020.acl-main.718"},{"key":"e_1_3_2_15_2","doi-asserted-by":"crossref","first-page":"1941","DOI":"10.1145\/2983323.2983879","volume-title":"Proceedings of the 25th ACM International on Conference on Information and Knowledge Management","author":"Gao Li","year":"2016","unstructured":"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\u20131944. DOI:10.1145\/2983323.2983879"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3226301"},{"key":"e_1_3_2_17_2","first-page":"172","volume-title":"CCF International Conference on Natural Language Processing and Chinese Computing","author":"Han Cuiyun","year":"2022","unstructured":"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\u2013183. DOI:10.1007\/978-3-031-17120-8_14"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCI.2023.3340617"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_3_2_20_2","doi-asserted-by":"crossref","first-page":"10834","DOI":"10.18653\/v1\/2023.emnlp-main.668","volume-title":"Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing","author":"Huang Guanhua","year":"2023","unstructured":"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\u201310852. DOI:10.18653\/v1\/2023.emnlp-main.668"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.findings-emnlp.32"},{"key":"e_1_3_2_22_2","volume-title":"3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7\u20139, 2015, Conference Track Proceedings","author":"Kingma Diederik P.","year":"2015","unstructured":"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\u20139, 2015, Conference Track Proceedings, Yoshua Bengio and Yann LeCun (Eds.). http:\/\/arxiv.org\/abs\/1412.6980"},{"key":"e_1_3_2_23_2","article-title":"Semi-supervised classification with graph convolutional networks","author":"Kipf Thomas N.","year":"2016","unstructured":"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","journal-title":"arXiv preprint arXiv:1609.02907"},{"key":"e_1_3_2_24_2","first-page":"282","volume-title":"ICML","author":"Lafferty John","year":"2001","unstructured":"John Lafferty, Andrew McCallum, and Fernando C. N. Pereira. 2001. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In ICML. 282\u2013289. https:\/\/repository.upenn.edu\/handle\/20.500.14332\/6188"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"e_1_3_2_26_2","first-page":"894","volume-title":"Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","author":"Li Sha","year":"2021","unstructured":"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\u2013908. DOI:10.18653\/v1\/2021.naacl-main.69"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.109234"},{"key":"e_1_3_2_28_2","first-page":"4985","volume-title":"Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","author":"Liang Yuan","year":"2022","unstructured":"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\u20134997. DOI:10.18653\/v1\/2022.naacl-main.367"},{"key":"e_1_3_2_29_2","first-page":"1532","volume-title":"2017 International Joint Conference on Neural Networks (IJCNN\u201917)","author":"Liu Chun-Yi","year":"2017","unstructured":"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\u201917). IEEE, 1532\u20131539. DOI:10.1109\/IJCNN.2017.7966033"},{"key":"e_1_3_2_30_2","first-page":"5216","volume-title":"Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","author":"Liu Xiao","year":"2022","unstructured":"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\u20135228. DOI:10.18653\/v1\/2022.acl-long.358"},{"key":"e_1_3_2_31_2","first-page":"1247","volume-title":"Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing","author":"Liu Xiao","year":"2018","unstructured":"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\u20131256. DOI:10.18653\/v1\/D18-1156"},{"key":"e_1_3_2_32_2","first-page":"12657","volume-title":"ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP\u201924)","author":"Liu Yuhan","year":"2024","unstructured":"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\u201924). IEEE, 12657\u201312661. DOI:10.1109\/ICASSP48485.2024.10446043"},{"key":"e_1_3_2_33_2","first-page":"6759","volume-title":"Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","author":"Ma Yubo","year":"2022","unstructured":"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\u20136774. https:\/\/aclanthology.org\/2022.acl-long.466\/"},{"key":"e_1_3_2_34_2","first-page":"63","volume-title":"Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations","author":"Min Bonan","year":"2021","unstructured":"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\u201371. DOI:10.18653\/v1\/2021.emnlp-demo.8"},{"key":"e_1_3_2_35_2","first-page":"1003","volume-title":"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","author":"Mintz Mike","year":"2009","unstructured":"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\u20131011. https:\/\/aclanthology.org\/P09-1113"},{"key":"e_1_3_2_36_2","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"32","author":"Nguyen Thien","year":"2018","unstructured":"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:10.1609\/aaai.v32i1.12039"},{"key":"e_1_3_2_37_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N16-1034"},{"key":"e_1_3_2_38_2","first-page":"2504","volume-title":"Proceedings of the 29th International Conference on Computational Linguistics","author":"Ren Yubing","year":"2022","unstructured":"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\u20132514. https:\/\/aclanthology.org\/2022.coling-1.221"},{"key":"e_1_3_2_39_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.12034"},{"key":"e_1_3_2_40_2","article-title":"Attention is all you need","volume":"30","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in Neural Information Processing Systems 30 (2017). https:\/\/arxiv.org\/abs\/1706.03762","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2024.3397321"},{"key":"e_1_3_2_42_2","first-page":"3533","volume-title":"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)","author":"Xu Runxin","year":"2021","unstructured":"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\u20133546. DOI:10.18653\/v1\/2021.acl-long.274"},{"key":"e_1_3_2_43_2","first-page":"5025","volume-title":"Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","author":"Xu Runxin","year":"2022","unstructured":"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\u20135036. https:\/\/aclanthology.org\/2022.naacl-main.370\/"},{"key":"e_1_3_2_44_2","first-page":"5766","volume-title":"Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP\u201919)","author":"Yan Haoran","year":"2019","unstructured":"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\u201919). Association for Computational Linguistics, Hong Kong, China, 5766\u20135770. DOI:10.18653\/v1\/D19-1582"},{"key":"e_1_3_2_45_2","doi-asserted-by":"crossref","first-page":"50","DOI":"10.18653\/v1\/P18-4009","volume-title":"Proceedings of ACL 2018, System Demonstrations","author":"Yang Hang","year":"2018","unstructured":"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\u201355. DOI:10.18653\/v1\/P18-4009"},{"key":"e_1_3_2_46_2","doi-asserted-by":"publisher","DOI":"10.1145\/3542925"},{"key":"e_1_3_2_47_2","first-page":"6298","volume-title":"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)","author":"Yang Hang","year":"2021","unstructured":"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\u20136308. DOI:10.18653\/v1\/2021.acl-long.492"},{"key":"e_1_3_2_48_2","doi-asserted-by":"crossref","first-page":"5284","DOI":"10.18653\/v1\/P19-1522","volume-title":"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics","author":"Yang Sen","year":"2019","unstructured":"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\u20135294. DOI:10.18653\/v1\/P19-1522"},{"key":"e_1_3_2_49_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCI.2023.3240087"},{"key":"e_1_3_2_50_2","doi-asserted-by":"crossref","first-page":"1753","DOI":"10.18653\/v1\/D15-1203","volume-title":"Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing","author":"Zeng Daojian","year":"2015","unstructured":"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\u20131762. DOI:10.18653\/v1\/D15-1203"},{"key":"e_1_3_2_51_2","doi-asserted-by":"crossref","first-page":"337","DOI":"10.18653\/v1\/D19-1032","volume-title":"Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP\u201919)","author":"Zheng Shun","year":"2019","unstructured":"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\u201919). Association for Computational Linguistics, Hong Kong, China, 337\u2013346. DOI:10.18653\/v1\/D19-1032"},{"key":"e_1_3_2_52_2","first-page":"4552","volume-title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22","author":"Zhu Tong","year":"2022","unstructured":"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\u20134558. DOI:10.24963\/ijcai.2022\/632Main Track."}],"container-title":["ACM Transactions on Asian and Low-Resource Language Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3698261","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3698261","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:17:19Z","timestamp":1750295839000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3698261"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,24]]},"references-count":51,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2024,10,31]]}},"alternative-id":["10.1145\/3698261"],"URL":"https:\/\/doi.org\/10.1145\/3698261","relation":{},"ISSN":["2375-4699","2375-4702"],"issn-type":[{"type":"print","value":"2375-4699"},{"type":"electronic","value":"2375-4702"}],"subject":[],"published":{"date-parts":[[2024,10,24]]},"assertion":[{"value":"2024-03-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-09-14","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-10-24","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}