1 |
Linguistic Data Consortium. ACE(automatic content extraction) English annotation guidelines for events version 5.4. 3[EB/OL]. [2022-12-07]. https://www.ldc.upenn.edu/.
|
2 |
XIANG W, WANG B. A survey of event extraction from text. IEEE Access, 2019, 7, 173111- 173137.
doi: 10.1109/ACCESS.2019.2956831
|
3 |
AHN D. The stages of event extraction[C]//Proceedings of Workshop on Annotating and Reasoning About Time and Events. New York, USA: ACM Press, 2006: 1-8.
|
4 |
李中秋, 洪宇, 王捷, 等. 基于实体画像增强网络的事件检测方法. 中文信息学报, 2022, 36 (8): 81- 91.
URL
|
|
LI Z Q, HONG Y, WANG J, et al. Entity profile enhancement network for event detection. Journal of Chinese Information Processing, 2022, 36 (8): 81- 91.
URL
|
5 |
陈斌, 周勇, 刘兵. 基于卷积双向长短期记忆网络的事件触发词抽取. 计算机工程, 2019, 45 (1): 153- 158.
doi: 10.3969/j.issn.1007-130X.2019.01.020
|
|
CHEN B, ZHOU Y, LIU B. Event trigger word extraction based on convolutional bidirectional long short term memory network. Computer Engineering, 2019, 45 (1): 153- 158.
doi: 10.3969/j.issn.1007-130X.2019.01.020
|
6 |
FERGUSON J, LOCKARD C, WELD D, et al. Semi-supervised event extraction with paraphrase clusters[C]//Proceedings of 2018 Conference of the North American Chapter of the Association for Computational Linguistic. Stroudsburg, USA: Association for Computational Linguistics, 2018: 359-364.
|
7 |
ARAKI J, MITAMURA T. Open-domain event detection using distant supervision[C]//Proceedings of the 27th International Conference on Computational Linguistics. Stroudsburg, USA: Association for Computational Linguistics, 2018: 878-891.
|
8 |
CAO Y X, HU Z K, CHUA T S, et al. Low-resource name tagging learned with weakly labeled data[C]//Proceedings of 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing. Stroudsburg, USA: Association for Computational Linguistics, 2019: 261-270.
|
9 |
CHEN Y B, LIU S L, ZHANG X, et al. Automatically labeled data generation for large scale event extraction[C]//Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. Stroudsburg, USA: Association for Computational Linguistics, 2017: 409-419.
|
10 |
WANG X, HAN X, LIU Z, et al. Adversarial training for weakly supervised event detection[C]//Proceedings of 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1(Long and Short Papers). Stroudsburg, USA: Association for Computational Linguistics, 2019: 998-1008.
|
11 |
ZENG Y, FENG Y, MA R, et al. Scale up event extraction learning via automatic training data generation[C]//Proceedings of the 32nd AAAI Conference on Artificial Intelligence and 30th Innovative Applications of Artificial Intelligence Conference and 8th AAAI Symposium on Educational Advances in Artificial Intelligence. Palo Alto, USA: AAAI Press, 2018: 6045-6052.
|
12 |
TONG M H, XU B, WANG S, et al. Improving event detection via open-domain trigger knowledge[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Stroudsburg, USA: Association for Computational Linguistics, 2020: 5887-5897.
|
13 |
JI H, GRISHMAN R. Refining event extraction through cross-document inference[C]//Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics. Stroudsburg, USA: Association for Computational Linguistics, 2008: 254-262.
|
14 |
QIN Y, ZHANG Y, ZHANG M, et al. Feature-rich segment-based news event detection on Twitter[C]//Proceedings of International Joint Conference on Natural Language Processing. Stroudsburg, USA: Association for Computational Linguistics, 2013: 302-310.
|
15 |
LIU S L, CHEN Y B, HE S Z, et al. Leveraging FrameNet to improve automatic event detection[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics(Volume 1: Long Papers). Stroudsburg, USA: Association for Computational Linguistics, 2016: 2134-2143.
|
16 |
LIU S L, CHEN Y B, LIU K, et al. Exploiting argument information to improve event detection via supervised attention mechanisms[C]//Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics(Volume 1: Long Papers). Stroudsburg, USA: Association for Computational Linguistics, 2017: 1789-1798.
|
17 |
CHEN Y B, XU L H, LIU K, et al. Event extraction via dynamic multi-pooling convolutional neural networks[C]//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). Stroudsburg, USA: Association for Computational Linguistics, 2015: 167-176.
|
18 |
NGUYEN T H, GRISHMAN R. Modeling skip-grams for event detection with convolutional neural networks[C]//Proceedings of 2016 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, USA: Association for Computational Linguistics, 2016: 886-891.
|
19 |
LIU X, LUO Z C, HUANG H Y. Jointly multiple events extraction via attention-based graph information aggregation[C]//Proceedings of 2018 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, USA: Association for Computational Linguistics, 2018: 1247-1256.
|
20 |
LAI V D, NGUYEN T N, NGUYEN T H. Event detection: gate diversity and syntactic importance scores for graph convolution neural networks[C]//Proceedings of 2020 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, USA: Association for Computational Linguistics, 2020: 5405-5411.
|
21 |
HU B, LIU Y, CHEN N Y, et al. SEGCN-DCR: a syntax-enhanced event detection framework with decoupled classification rebalance. Neurocomputing, 2022, 481, 55- 66.
doi: 10.1016/j.neucom.2022.01.069
|
22 |
LIU J, CHEN Y B, LIU K, et al. How does context matter? On the robustness of event detection with context-selective mask generalization[C]//Proceedings of the Findings of the Association for Computational Linguistics. Stroudsburg, USA: Association for Computational Linguistics, 2020: 2523-2532.
|
23 |
LI R, ZHAO W L, YANG C, et al. Treasures outside contexts: improving event detection via global statistics[C]//Proceedings of 2021 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, USA: Association for Computational Linguistics, 2021: 2625-2635.
|
24 |
CAO K, WEI C, GAIDON A, et al. Learning imbalanced datasets with label-distribution-aware margin loss[C]//Proceedings of the 33rd International Conference on Neural Information Processing Systems. Berlin, Germany: Springer, 2019: 1567-1578.
|
25 |
KHAN S H, HAYAT M, BENNAMOUN M, et al. Cost-sensitive learning of deep feature representations from imbalanced data. IEEE Transactions on Neural Networks and Learning Systems, 2018, 29 (8): 3573- 3587.
doi: 10.1109/TNNLS.2017.2732482
|
26 |
PETRONI F, ROCKTÄSCHEL T, RIEDEL S, et al. Language models as knowledge bases?[C]//Proceedings of 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing. Stroudsburg, USA: Association for Computational Linguistics, 2019: 2463-2473.
|
27 |
|
28 |
苗佳, 段跃兴, 张月琴, 等. 基于CNN-BiGRU模型的事件触发词抽取方法. 计算机工程, 2021, 47 (9): 69-74, 83.
URL
|
|
MIAO J, DUAN Y X, ZHANG Y Q, et al. Method for extracting event trigger words based on the CNN-BiGRU model. Computer Engineering, 2021, 47 (9): 69-74, 83.
URL
|
29 |
NGUYEN T H, CHO K, GRISHMAN R. Joint event extraction via recurrent neural networks[C]//Proceedings of 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Stroudsburg, USA: Association for Computational Linguistics, 2016: 300-309.
|
30 |
SHA L, QIAN F, CHANG B, et al. Jointly extracting event triggers and arguments by dependency-bridge RNN and tensor-based argument interaction[C]//Proceedings of the 32nd AAAI Conference on Artificial Intelligence and 30th Innovative Applications of Artificial Intelligence Conference and 8th AAAI Symposium on Educational Advances in Artificial Intelligence. Palo Alto, USA: AAAI Press, 2018: 5916-5923.
|
31 |
NGUYEN T H, GRISHMAN R. Graph convolutional networks with argument-aware pooling for event detection[C]//Proceedings of the 32nd AAAI Conference on Artificial Intelligence and 30th Innovative Applications of Artificial Intelligence Conference and 8th AAAI Symposium on Educational Advances in Artificial Intelligence. Palo Alto, USA: AAAI Press, 2018: 5900-5907.
|
32 |
CUI S Y, YU B W, LIU T W, et al. Edge-enhanced graph convolution networks for event detection with syntactic relation[C]//Proceedings of the Findings of the Association for Computational Linguistics. Stroudsburg, USA: Association for Computational Linguistics, 2020: 2329-2339.
|