[1] Liu K, Chen Y B, Liu J, Zuo X Y, Zhao J. Extracting events and their relations from texts: A survey on recent research progress and challenges. AI Open, 2020, 1: 22–39 doi: 10.1016/j.aiopen.2021.02.004
[2] 王县县, 禹龙, 田生伟, 王瑞锦. 独立RNN 和胶囊网络的维吾尔语事件缺失元素填充. 自动化学报, 2021, 47(4): 903–912
Wang Xian-Xian, Yu Long, Tian Sheng-Wei, Wang Rui-Jin. Missing argument fllling of uyghur event based on independent recurrent neural network and capsule network. Acta Automatica Sinica, 2021, 47(4): 903–912
[3] 王梦来, 李想, 陈奇, 李澜博, 赵衍运. 基于CNN的监控视频事件检测. 自动化学报, 2016, 42(6): 892–903 doi: 10.16383/j.aas.2016.c150729
Wang Meng-Lai, Li Xiang, Chen Qi, Li Lan-Bo, Zhao Yan-Yun. Surveillance event detection based on CNN. Acta Automatica Sinica, 2016, 42(6): 892–903 doi: 10.16383/j.aas.2016.c150729
[4] 介飞, 谢飞, 李磊, 吴信东. 社交网络中隐式事件突发性检测. 自动化学报, 2018, 44(4): 730–742 doi: 10.16383/j.aas.2017.c160564
Jie Fei, Xie Fei, Li Lei, Wu Xin-Dong. Latent event-related burst detection in social networks. Acta Automatica Sinica, 2018, 44(4): 730–742 doi: 10.16383/j.aas.2017.c160564
[5] Valenzuela-Escarcega M A, Hahn-Powell G, Surdeanu M, Hicks T. A domain-independent rule-based framework for event extraction. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing. Beijing, China: Association for Computational Linguistics, 2015. 127–132
[6] Sha L, Liu J, Lin C Y, Li S J, Chang B B, Sui Z F. RBPB: Regularization-based pattern balancing method for event extraction. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. Berlin, Germany: Association for Computational Linguistics, 2016. 1224–1234
[7] Hsi A, Yang Y M, Carbonell J, Xu R C. Leveraging multilingual training for limited resource event extraction. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers. Osaka, Japan: Association for Computational Linguistics, 2016. 1201–1210
[8] Li F, Zhang Y, Zhang M S, Ji D H. Joint models for extracting adverse drug events from biomedical text. In: Proceedings of the 25th International Joint Conference on Artificial Intelligence. New York, USA: Springer, 2016. 2838–2844
[9] Nguyen T H, Cho K, Grishman R. Joint event extraction via recurrent neural networks. In: Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. San Diego, USA: Association for Computational Linguistics, 2016. 300–309
[10] Badgett A, Huang R H. Extracting subevents via an effective two-phase approach. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. Austin, USA: Association for Computational Linguistics, 2016. 906–911
[11] Chen Y B, Liu S L, Zhang X, Liu K, Zhao J. Automatically labeled data generation for large scale event extraction. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. Vancouver, Canada: Association for Computational Linguistics, 2017. 409–419
[12] Sha L, Qian F, Chang B B, Sui Z F. Jointly extracting event triggers and arguments by dependency-bridge RNN and tensor-based argument interaction. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence. New Orleans, USA: AAAI, 2018. 5916–5923
[13] Zeng Y, Feng Y S, Ma R, Wang Z, Yan R, Shi C D, et al. Scale up event extraction learning via automatic training data generation. In: Proceedings of the 32nd AAAI, 2018. 6045–6052
[14] Huang L F, Ji H, Cho K, Dagan I, Riedel S, Voss C R. Zero-shot transfer learning for event extraction. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics. Melbourne, Australia: Association for Computational Linguistics, 2018. 2160–2170
[15] Liu X, Luo Z C, Huang H Y. Jointly multiple events extraction via attention-based graph information aggregation. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. Brussels, Belgium: Association for Computational Linguistics, 2018. 1247–1256
[16] Subburathinam A, Lu D, Ji H, May J, Chang S F, Sil A, et al. Cross-lingual structure transfer for relation and event extraction. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing. Hong Kong, China: Association for Computational Linguistics, 2019. 313–325
[17] Zhang J C, Qin Y X, Zhang Y, Liu M C, Ji D H. Extracting entities and events as a single task using a transition-based neural model. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence. Macao, China: Springer, 2019. 5422–5428
[18] Yang S, Feng D W, Qiao L B, Kan Z G, Li D S. Exploring pre-trained language models for event extraction and generation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Florence, Italy: Association for Computational Linguistics, 2019. 5284–5294
[19] 贺瑞芳, 段邵杨. 基于多任务学习的中文事件抽取联合模型. 软件学报, 2019, 30(4): 1015–1030 doi: 10.13328/j.cnki.jos.005380
He Rui-Fang, Duan Shao-Yang. Joint Chinese event extraction based multi-task learning. Journal of Software, 2019, 30(4): 1015–1030 doi: 10.13328/j.cnki.jos.005380
[20] Li D Y, Huang L F, Ji H, Han J W. Biomedical event extraction based on knowledge-driven tree-LSTM. In: Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Minneapolis, USA: Association for Computational Linguistics, 2019. 1421–1430
[21] Huang P X, Zhao X, Takanobu R, Zhen T, Xiao W D. Joint event extraction with hierarchical policy network. In: Proceedings of the 28th International Conference on Computational Linguistics. Barcelona, Spain: Association for Computational Linguistics, 2020. 2653–2664
[22] Du X Y, Cardie C. Event extraction by answering (almost) natural questions. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. Virtual Event: Association for Computational Linguistics, 2020. 671–683
[23] Li F Y, Peng W H, Chen Y G, Wang Q, Pan L, Lyu Y J, et al. Event extraction as multi-turn question answering. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. Virtual Event: Association for Computational Linguistics, 2020. 829–838
[24] Liu J, Chen Y B, Liu K, Bi W, Liu X J. Event extraction as machine reading comprehension. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. Virtual Event: Association for Computational Linguistics, 2020. 1641–1651
[25] Ma J, Wang S, Anubhat R, Ballesteros M, Al-Onaizan Y. Resource-enhanced neural model for event argument extraction. In: Findings of the Association for Computational Linguistics: EMNLP. Virtual Event: Association for Computational Linguistics, 2020. 3554–3559
[26] Abdulkadhar S, Bhasuran B, Natarajan J. Multiscale Laplacian graph kernel combined with lexico-syntactic patterns for biomedical event extraction from literature. Knowledge and Information Systems, 2021, 63: 143–173 doi: 10.1007/s10115-020-01514-8
[27] Li Q, Peng H, Li J X, Wu J, Ning Y X, Wang L H, et al. Reinforcement learning-based dialogue guided event extraction to exploit argument relations. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2022, 30: 520–533 doi: 10.1109/TASLP.2021.3138670
[28] Zheng S, Cao W, Xu W, Bian J. Doc2EDAG: An end-to-end document-level framework for Chinese financial event extraction. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing. Hong Kong, China: Association for Computational Linguistics, 2019. 337–346
[29] Li S, Ji H, Han J W. Document-level event argument extraction by conditional generation. In: Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Virtual Event: Association for Computational Linguistics, 2021. 894–908
[30] Du X Y, Cardie C. Document-level event role filler extraction using multi-granularity contextualized encoding. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Virtual Event: Association for Computational Linguistics, 2020. 8010–8020
[31] Zhao Y, Jin X L, Wang Y Z, Cheng X Q. Document embedding enhanced event detection with hierarchical and supervised attention. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics. Melbourne, Australia: Association for Computational Linguistics, 2018. 414–419
[32] Yang B S, Mitchell T. Joint extraction of events and entities within a document context. In: Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. San Diego, USA: Association for Computational Linguistics, 2016. 289–299
[33] Yang H, Chen Y B, Liu K, Xiao Y, Zhao J. DCFEE: A document-level Chinese financial event extraction system based on automatically labeled training data. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics-System Demonstrations. Melbourne, Australia: Association for Computational Linguistics, 2018. 1–6
[34] Zhao W Z, Zhang J Y, Yang J C, He T T, Ma H F, Li Z X. A novel joint biomedical event extraction framework via two-level modeling of documents. Information Sciences, 2021, 550: 27–40 doi: 10.1016/j.ins.2020.10.047
[35] Zhang Z S, Kong X, Liu Z Z, Ma X Z, Hovy E. A two-step approach for implicit event argument detection. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Virtual Event: Association for Computational Linguistics, 2020. 7479–7485
[36] Ebner S, Xia P, Culkin R, Rawlins K, Durme B V. Multi-sentence argument linking. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Virtual Event: Association for Computational Linguistics, 2020. 8057–8077
[37] Liu S L, Chen Y B, He S Z, Liu K, Zhao J. Leveraging framenet to improve automatic event detection. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. Berlin, Germany: Association for Computational Linguistics, 2016. 2134–2143
[38] Zhang C, Soderland S, Weld D S. Exploiting parallel news streams for unsupervised event extraction. Transactions of the Association for Computational Linguistics, 2015, 3: 117–129 doi: 10.1162/tacl_a_00127
[39] Ferguson J, Lockard C, Weld D S, Hajishirzi H. Semi-supervised event extraction with paraphrase clusters. In: Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. New Orleans, USA: Association for Computational Linguistics, 2018. 359–364
[40] Liu J, Chen Y B, Liu K, Zhao J. Event detection via gated multilingual attention mechanism. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence. New Orleans, USA: AAAI, 2018. 4865–4872
[41] Lee K, Qadir A, Hasan S A, Datla V, PRAKASH A, LIU J, et al. Adverse drug event detection in tweets with semi-supervised convolutional neural networks. In: Proceedings of the Web Conference. Perth, Australia: ACM, 2017. 705–714
[42] Peng H R, Song Y Q, Roth D. Event detection and co-reference with minimal supervision. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. Austin, USA: Association for Computational Linguistics, 2016. 392–402
[43] Deng S M, Zhang N Y, Kang J J, Zhang Y C, Zhang W, Chen H J. Meta-learning with dynamic-memory-based prototypical network for few-shot event detection. In: Proceedings of the 13th International Conference on Web Search and Data Mining. Houston, USA: ACM, 2020. 151–159
[44] Lai V D, Nguyen M V, Nguyen T H, Dernoncourt F. Graph learning regularization and transfer learning for few-shot event detection. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. Virtual Event: ACM, 2021. 2172–2176
[45] Yuan Q, Ren X, He W Q, Zhang C, Geng X H, Huang L F, et al. Open-schema event profiling for massive news corpora. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management. Torino, Italy: ACM, 2018. 587–596
[46] Shen S R, Qi G L, Li Z, Bi S, Wang L S. Hierarchical Chinese legal event extraction via pedal attention mechanism. In: Proceedings of the 28th International Conference on Computational Linguistics. Barcelona, Spain: Association for Computational Linguistics, 2020. 100–113
[47] Bhardwaj A, Yang J, Cudre-Mauroux P. A human-AI loop approach for joint keyword discovery and expectation estimation in micropost event detection. In: Proceedings of the 34th AAAI Conference on Artificial Intelligence. New York, USA: AAAI, 2020. 2451–2458
[48] Hogemnoom F, Frasincar F, Kaymak U, Jong F D, Caron E. A survey of event extraction methods from text for decision support systems. Decision Support Systems, 2016, 85: 12–22 doi: 10.1016/j.dss.2016.02.006
[49] Li Q, Li J X, Sheng J W, Cui S Y, Wu J, Hei Y M, et al. A survey on deep learning event extraction: Approaches and applications. IEEE Transactions on Neural Networks and Learning Systems, 2023, 34: 1–21 doi: 10.1109/TNNLS.2023.3305210
[50] 黄河燕, 刘啸. 面向新领域的事件抽取研究综述. 智能系统学报, 2022, 17(1): 201–212
Huang He-Yan, Liu Xiao. A survey on event extraction in new domains. CAAI Transactions on Intelligent Systems, 2022, 17(1): 201–212