Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (10): 2990-2995.DOI: 10.11772/j.issn.1001-9081.2021081521
• Artificial intelligence • Previous Articles Next Articles
Ping LUO1, Ling DING1, Xue YANG2, Yang XIANG1
Received:
2021-08-26
Revised:
2021-12-03
Accepted:
2021-12-06
Online:
2022-01-07
Published:
2022-10-10
Contact:
Yang XIANG
About author:
LUO Ping, born in 1997, M. S. candidate. Her research interests include natural language processing, information extraction, event extraction.Supported by:
通讯作者:
向阳
作者简介:
第一联系人:罗萍(1997—),女,安徽黄山人,硕士研究生,主要研究方向:自然语言处理、信息抽取、事件抽取基金资助:
CLC Number:
Ping LUO, Ling DING, Xue YANG, Yang XIANG. Chinese event detection based on data augmentation and weakly supervised adversarial training[J]. Journal of Computer Applications, 2022, 42(10): 2990-2995.
罗萍, 丁玲, 杨雪, 向阳. 基于数据增强和弱监督对抗训练的中文事件检测[J]. 《计算机应用》唯一官方网站, 2022, 42(10): 2990-2995.
Add to citation manager EndNote|Ris|BibTeX
URL: http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2021081521
模型 | P | R | F1 |
---|---|---|---|
HNN[ | 77.10 | 53.10 | 63.00 |
NPN[ | 60.90 | 69.30 | 64.80 |
TLNN[ | 64.45 | 71.47 | 67.78 |
HCBNN[ | 66.40 | 76.00 | 70.90 |
BMAD | 73.94 | 69.67 | 71.74 |
Tab. 1 Experimental results on trigger classification task on ACE2005
模型 | P | R | F1 |
---|---|---|---|
HNN[ | 77.10 | 53.10 | 63.00 |
NPN[ | 60.90 | 69.30 | 64.80 |
TLNN[ | 64.45 | 71.47 | 67.78 |
HCBNN[ | 66.40 | 76.00 | 70.90 |
BMAD | 73.94 | 69.67 | 71.74 |
模型 | P | R | F1 |
---|---|---|---|
Baseline | 71.78 | 65.66 | 68.59 |
Baseline + Semi | 72.80 | 66.42 | 69.46 |
Baseline + Mix | 75.49 | 67.17 | 71.09 |
Baseline + Semi + Mix | 72.97 | 69.67 | 71.28 |
BMAD (Baseline + Semi + Mix + Adv) | 73.94 | 69.67 | 71.74 |
Tab. 2 Ablation experimental results
模型 | P | R | F1 |
---|---|---|---|
Baseline | 71.78 | 65.66 | 68.59 |
Baseline + Semi | 72.80 | 66.42 | 69.46 |
Baseline + Mix | 75.49 | 67.17 | 71.09 |
Baseline + Semi + Mix | 72.97 | 69.67 | 71.28 |
BMAD (Baseline + Semi + Mix + Adv) | 73.94 | 69.67 | 71.74 |
1 | 贺瑞芳, 段绍杨. 基于多任务学习的中文事件抽取联合模型[J]. 软件学报, 2019, 30(4):1015-1030. |
HE R F, DUAN S Y. Joint Chinese event extraction based multi-task learning[J]. Journal of Software, 2019, 30(4): 1015-1030. | |
2 | YANG H, CHUA T S, WANG S G, et al. Structured use of external knowledge for event-based open domain question answering[C]// Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2003:33-40. 10.1145/860435.860444 |
3 | BASILE P, CAPUTO A, SEMERARO G, et al. Time event extraction to boost an information retrieval system[M]// LAI C, GIULIANI A, SEMERARO G. Information Filtering and Retrieval, SCI 668. Cham: Springer International Publishing, 2017 :1-12. |
4 | CHENG P X, ERK K. Implicit argument prediction with event knowledge[C]// Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). Stroudsburg, PA: Association for Computational Linguistics, 2018: 831-840. 10.18653/v1/n18-1076 |
5 | AHN D. The stages of event extraction[C]// Proceedings of the 2006 Workshop on Annotating and Reasoning about Time and Events. Stroudsburg, PA: Association for Computational Linguistics, 2006: 1-8. 10.3115/1629235.1629236 |
6 | JI H, GRISHMAN R. Refining event extraction through cross-document inference[C]// Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. Stroudsburg, PA: Association for Computational Linguistics, 2008: 254-262. 10.3115/1564169 |
7 | LI Q, JI H, HUANG L. Joint event extraction via structured prediction with global features[C]// Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Stroudsburg, PA: Association for Computational Linguistics, 2013: 73-82. |
8 | ARAKI J, MITAMURA T. Joint event trigger identification and event coreference resolution with structured perceptron[C]// Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA: Association for Computational Linguistics, 2015: 2074-2080. 10.18653/v1/d15-1247 |
9 | NGUYEN T H, GRISHMAN R. Event detection and domain adaptation with 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 2: Short Papers). Stroudsburg, PA: Association for Computational Linguistics, 2015: 365-371. 10.3115/v1/p15-2060 |
10 | GHAEINI R, FERN X, HUANG L, et al. Event nugget detection with forward-backward recurrent neural networks[C]// Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Stroudsburg, PA: Association for Computational Linguistics, 2016: 369-373. 10.18653/v1/p16-2060 |
11 | WADDEN D, WENNBERG U, LUAN Y, et al. Entity, relation, and event extraction with contextualized span representations[C]// Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing. Stroudsburg, PA: Association for Computational Linguistics, 2019: 5784-5789. 10.18653/v1/d19-1585 |
12 | CAO P F, CHEN Y B, ZHAO J, et al. Incremental event detection via knowledge consolidation networks[C]// Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA: Association for Computational Linguistics, 2020: 707-717. 10.18653/v1/2020.emnlp-main.52 |
13 | WANG Z Q, WANG X Z, HAN X, et al. CLEVE: contrastive pre-training for event extraction[C]// 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). Stroudsburg, PA: Association for Computational Linguistics, 2021: 6283-6297. 10.18653/v1/2021.acl-long.491 |
14 | XIE Q Z, DAI Z H, HOVY E, et al. Unsupervised data augmentation for consistency training[C/OL]// Proceedings of the 34th Conference on Neural Information Processing Systems. [2021-04-29].. |
15 | ABDULMUMIN I, GALADANCI B S, ISA A. Iterative batch back-translation for neural machine translation: a conceptual model[EB/OL]. (2019-11-26) [2021-10-10].. 10.1007/s10590-021-09284-y |
16 | PATWARDHAN S, RILOFF E. A unified model of phrasal and sentential evidence for information extraction[C]// Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA: Association for Computational Linguistics, 2009: 151-160. 10.3115/1699510.1699530 |
17 | LIAO S S, GRISHMAN R. Using document level cross-event inference to improve event extraction[C]// Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics. Stroudsburg, PA: Association for Computational Linguistics, 2010: 789-797. |
18 | McCLOSKY D, SURDEANU M, MANNING C D. Event extraction as dependency parsing[C]// Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. Stroudsburg, PA: Association for Computational Linguistics, 2011: 1626-1635. |
19 | HONG Y, ZHANG J F, MA B, et al. Using cross-entity inference to improve event extraction[C]// Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. Stroudsburg, PA: Association for Computational Linguistics, 2011: 1127-1136. |
20 | HUANG R H, RILOFF E. Modeling textual cohesion for event extraction[C]// Proceedings of the 26th AAAI Conference on Artificial Intelligence. Palo Alto, CA: AAAI Press, 2012: 1664-1670. |
21 | LI Q, JI H, HONG Y, et al. Constructing information networks using one single model[C]// Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA: Association for Computational Linguistics, 2014: 1846-1851. 10.3115/v1/d14-1198 |
22 | 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, PA: Association for Computational Linguistics, 2015: 167-176. 10.3115/v1/p15-1017 |
23 | NGUYEN T H, GRISHMAN R. Modeling skip-grams for event detection with convolutional neural networks[C]// Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA: Association for Computational Linguistics, 2016: 886-891. 10.18653/v1/d16-1085 |
24 | 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, PA: Association for Computational Linguistics, 2017: 1789-1798. 10.18653/v1/p17-1164 |
25 | LIU S B, CHENG R, YU X M, et al. Exploiting contextual information via dynamic memory network for event detection[C]// Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA: Association for Computational Linguistics, 2018: 1030-1035. 10.18653/v1/d18-1127 |
26 | YAN H R, JIN X L, MENG X B, et al. Event detection with multi-order graph convolution and aggregated attention[C]// Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing. Stroudsburg, PA: Association for Computational Linguistics, 2019: 5766-5770. 10.18653/v1/d19-1582 |
27 | WANG Z H, GUO Y, WANG J H. Empower Chinese event detection with improved atrous convolution neural networks[J]. Neural Computing and Applications, 2021, 33(11): 5805-5820. 10.1007/s00521-020-05360-1 |
28 | 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 (Volume 1: Long Papers). Stroudsburg, PA: Association for Computational Linguistics, 2017: 409-419. 10.18653/v1/p17-1038 |
29 | ARAKI J, MITAMURA T. Open-domain event detection using distant supervision[C]// Proceedings of the 27th International Conference on Computational Linguistics. Stroudsburg, PA: Association for Computational Linguistics, 2018: 878-891. |
30 | ZENG Y, FENG Y S, MA R, et al. Scale up event extraction learning via automatic training data generation[C]// Proceedings of the 32nd AAAI Conference on Artificial Intelligence. Palo Alto, CA: AAAI Press, 2018: 6045-6052. 10.1609/aaai.v32i1.12030 |
31 | HUANG L F, JI H. Semi-supervised new event type induction and event detection[C]// Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA: Association for Computational Linguistics, 2020: 718-724. 10.18653/v1/2020.emnlp-main.53 |
32 | SHAO Z H, SHANG L F, LIU Q, et al. A mutual information maximization approach for the spurious solution problem in weakly supervised question answering[C]// 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). Stroudsburg, PA: Association for Computational Linguistics, 2021: 4111-4124. 10.18653/v1/2021.acl-long.318 |
33 | GOOGFELLOW I J, POUGET-ABADIE J, MIRZA M, et al. Generative adversarial nets[C]// Proceedings of the 27th International Conference on Neural Information Processing Systems. Cambridge: MIT Press, 2014:2672-2680. |
34 | HONG Y, ZHOU W X, ZHANG J L, et al. Self-regulation: employing a generative adversarial network to improve event detection[C]// Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Stroudsburg, PA: Association for Computational Linguistics, 2018: 515-526. 10.18653/v1/p18-1048 |
35 | WANG X Z, HAN X, LIU Z Y, et al. Adversarial training for weakly supervised event detection[C]// Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Stroudsburg, PA: Association for Computational Linguistics, 2019: 998-1008. 10.18653/v1/n18-2 |
36 | MA X Y, SHEN Y L, FANG G F, et al. Adversarial self-supervised data-free distillation for text classification[C]// Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA: Association for Computational Linguistics, 2020: 6182-6192. 10.18653/v1/2020.emnlp-main.499 |
37 | DEVLIN J, CHANG M W, LEE K, et al. BERT: pre-training of deep bidirectional trans-formers for language understanding[C]// Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Stroudsburg, PA: Association for Computational Linguistics, 2019: 4171-4186. 10.18653/v1/n18-2 |
38 | YU J T, BOHNET B, POESIO M. Named entity recognition as dependency parsing[C]// Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Stroudsburg, PA: Association for Computational Linguistics, 2020: 6470-6476. 10.18653/v1/2020.acl-main.577 |
39 | CHEN J A, YANG Z C, YANG D Y. MixText: linguistically-informed interpolation of hidden space for semi-supervised text classification[C]// Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Stroudsburg, PA: Association for Computational Linguistics, 2020: 2147-2157. 10.18653/v1/2020.acl-main.194 |
40 | CHEN J A, WANG Z H, TIAN R, et al. Local additivity based data augmentation for semi-supervised NER[C]// Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA: Association for Computational Linguistics, 2020: 1241-1251. 10.18653/v1/2020.emnlp-main.95 |
41 | FENG X C, QIN B, LIU T. A language-independent neural network for event detection[J]. Science China (Information Sciences), 2018, 61(9): No.92106. 10.1007/s11432-017-9359-x |
42 | LIN H Y, LU Y J, HAN X P, et al. Nugget proposal networks for Chinese event detection[C]// Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Stroudsburg, PA: Association for Computational Linguistics, 2018: 1565-1574. 10.18653/v1/p18-1145 |
43 | DING N, LI Z R, LIU Z Y, et al. Event detection with trigger-aware lattice neural network[C]// Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing. PA: Association for Computational Linguistics, 2019: 347-356. 10.18653/v1/d19-1033 |
44 | XI X Y, ZHANG T, YE W, et al. A hybrid character representation for Chinese event detection[C]// Proceedings of the 2019 International Joint Conference on Neural Networks. Piscataway: IEEE, 2019: 1-8. 10.1109/ijcnn.2019.8851786 |
[1] | Yimin CAO, Lei CAI, Jingyang GAO. Gene data generation method based on generative adversarial network [J]. Journal of Computer Applications, 2022, 42(3): 783-790. |
[2] | Qiujie SUN, Jinggui LIANG, Si LI. Chinese grammatical error correction model based on bidirectional and auto-regressive transformers noiser [J]. Journal of Computer Applications, 2022, 42(3): 860-866. |
[3] | Yu PENG, Yaolian SONG, Jun YANG. Motor imagery electroencephalography classification based on data augmentation [J]. Journal of Computer Applications, 2022, 42(11): 3625-3632. |
[4] | Shuang DENG, Xiaohai HE, Linbo QING, Honggang CHEN, Qizhi TENG. Weakly supervised fine-grained classification method of Alzheimer’s disease based on improved visual geometry group network [J]. Journal of Computer Applications, 2022, 42(1): 302-309. |
[5] | JIA Chengxun, LAI Hua, YU Zhengtao, WEN Yonghua, YU Zhiqiang. Chinese-Vietnamese pseudo-parallel corpus generation based on monolingual language model [J]. Journal of Computer Applications, 2021, 41(6): 1652-1658. |
[6] | GAN Lan, SHEN Hongfei, WANG Yao, ZHANG Yuejin. Data augmentation method based on improved deep convolutional generative adversarial networks [J]. Journal of Computer Applications, 2021, 41(5): 1305-1313. |
[7] | LU Xinwei, YU Pengfei, LI Haiyan, LI Hongsong, DING Wenqian. Weakly supervised fine-grained image classification algorithm based on attention-attention bilinear pooling [J]. Journal of Computer Applications, 2021, 41(5): 1319-1325. |
[8] | CUI Bowen, JIN Tao, WANG Jianmin. Overview of information extraction of free-text electronic medical records [J]. Journal of Computer Applications, 2021, 41(4): 1055-1063. |
[9] | HUO Shoujun, HAO Yan, SHI Huiyu, DONG Yanqing, CAO Rui. Pattern recognition of motor imagery EEG based on deep convolutional network [J]. Journal of Computer Applications, 2021, 41(4): 1042-1048. |
[10] | JIANG Ning, FANG Jinglong, YANG Qing. Global-local domain adaptive object detection based on single shot multibox detector [J]. Journal of Computer Applications, 2021, 41(2): 517-522. |
[11] | Yunpeng GONG, Zhiyong ZENG, Feng YE. Person re-identification method based on grayscale feature enhancement [J]. Journal of Computer Applications, 2021, 41(12): 3590-3595. |
[12] | CHEN Li, WANG Hongyuan, ZHANG Yunpeng, CAO Liang, YIN Yuchang. Video-based person re-identification method by jointing evenly sampling-random erasing and global temporal feature pooling [J]. Journal of Computer Applications, 2021, 41(1): 164-169. |
[13] | CHEN Foji, ZHU Feng, WU Qingxiao, HAO Yingming, WANG Ende. Infrared image data augmentation based on generative adversarial network [J]. Journal of Computer Applications, 2020, 40(7): 2084-2088. |
[14] | CHENG Guangtao, GONG Jiachang, LI Jian. Smoke recognition method based on dense convolutional neural network [J]. Journal of Computer Applications, 2020, 40(5): 1465-1469. |
[15] | FAN Wei, DUAN Bokun, HUANG Rui, LIU Ting, ZHANG Ning. Interactive augmentation method for aircraft engine borescope inspection images based on style transfer [J]. Journal of Computer Applications, 2020, 40(12): 3631-3636. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||