Chinese Document-Level Emergency Event Extraction Dataset and Corresponding Methods
Source abstract
Abstract
Structured extraction of emergency event information can effectively enhance the ability to respond to emergency events. This article focuses on extracting Chinese document-level emergency events, which entails addressing two key issues in this field: related datasets and the problem of role overlapping between candidate entities, which has been overlooked in existing DEE (document-level event extraction) studies that predominantly employed sequence annotation for candidate entity extraction. To tackle these challenges, we constructed a Chinese document-level emergency extraction dataset (CDEEE) and provides annotations for argument scattering, multiple events, and role overlapping. Additionally, a model named RODEE is proposed to address the role overlapping problem in DEE tasks. RODEE employs two independent modules to represent the head and tail positions of candidate entities, and utilizes a multiplication attention mechanism to interact between the two, generating a scoring matrix. Subsequently, role-overlapping candidate entities are predicted to facilitate the completion of DEE tasks. Experiments were conducted on our manually annotated dataset, CDEEE, and the results show that RODEE effectively solves the problem of role overlapping among candidate entities, resulting in improved performance of the DEE model, with an F1 value of 77.7%.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Topic Modeling · Natural Language Processing Techniques · Advanced Text Analysis Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Information Science and Engineering, Xinjiang University, Urumqi 830017, China
Xinjiang Key Laboratory of Multilingual Information Technology, Xinjiang University, Urumqi 830017, China
Funding
the Natural Science Foundation of China · the Autonomous Region Science and Technology Program
References (30)
Show source-provided references
1. Media Exposure and Individuals’ Emergency Preparedness Behaviors for Coping with Natural and Human-Made Disasters10.1016/j.jenvp.2019.04.005 ↗
2. Liu, J., Chen, Y., Liu, K., Bi, W., and Liu, X. (2020, January 16–20). Event extraction as machine reading comprehension. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Online.10.18653/v1/2020.emnlp-main.128 ↗
3. Huang, L., Ji, H., Cho, K., and Voss, C.R. (2017). Zero-shot transfer learning for event extraction. arXiv.10.18653/v1/P18-1201 ↗
4. Hong, Y., Zhang, J., Ma, B., Yao, J., Zhou, G., and Zhu, Q. (2011, January 19–24). Using Cross-Entity Inference to Improve Event Extraction. Proceedings of the Meeting of the Association for Computational Linguistics: Human Language Technologies, Portland, OR, USA.
5. Doddington, G., Mitchell, A., Przybocki, M., Ramshaw, L., Strassel, S., and Weischedel, R. (2004, January 26–28). The Automatic Content Extraction (ACE) Program Tasks, Data, and Evaluation. Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04), Lisbon, Portugal.
6. Grishman, R., and Sundheim, B. (1996, January 5–9). Message Understanding Conference-6: A Brief History. Proceedings of the 16th Conference on Computational Linguistics, Copenhagen, Denmark.10.3115/992628.992709 ↗
7. Li, S., Ji, H., and Han, J. (2021). Document-Level Event Argument Extraction by Conditional Generation. arXiv.10.18653/v1/2021.naacl-main.69 ↗
8. Yang, H., Chen, Y., Liu, K., Xiao, Y., and Zhao, J. (2018, January 15–20). DCFEE: A Document-Level Chinese Financial Event Extraction System Based on Automatically Labeled Training Data. Proceedings of the Meeting of the Association for Computational Linguistics, Melbourne, Australia.10.18653/v1/P18-4009 ↗
9. Zheng, S., Cao, W., Xu, W., and Bian, J. (2019, January 3–7). Doc2EDAG: An End-to-End Document-Level Framework for Chinese Financial Event Extraction. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), Hong Kong, China.10.18653/v1/D19-1032 ↗
10. Yang, H., Sui, D., Chen, Y., Liu, K., Zhao, J., and Wang, T. (2021, January 1–6). Document-Level Event Extraction via Parallel Prediction Networks. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, Online.10.18653/v1/2021.acl-long.492 ↗
11. Cui, Y., Che, W., Liu, T., Qin, B., Yang, Z., Wang, S., and Hu, G. (2021). Pre-Training with Whole Word Masking for Chinese BERT, Institute of Electrical and Electronics Engineers (IEEE).10.1109/TASLP.2021.3124365 ↗
12. Ebner, S., Xia, P., Culkin, R., Rawlins, K., and Van Durme, B. (2020, January 5–10). Multi-Sentence Argument Linking. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Online.10.18653/v1/2020.acl-main.718 ↗
13. Du, X., and Cardie, C. (2020, January 5–10). Document-Level Event Role Filler Extraction Using Multi-Granularity Contextualized Encoding. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Online.10.18653/v1/2020.acl-main.714 ↗
14. Du, X., Rush, A., and Cardie, C. (2021, January 19–23). GRIT: Generative Role-Filler Transformers for Document-Level Event Entity Extraction. Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, Online.10.18653/v1/2021.eacl-main.52 ↗
15. Huang, K.-H., and Peng, N. (2021, January 11). Document-Level Event Extraction with Efficient End-to-End Learning of Cross-Event Dependencies. Proceedings of the Third Workshop on Narrative Understanding, Virtual.10.18653/v1/2021.nuse-1.4 ↗
16. Huang, Y., and Jia, W. (2021, January 16–20). Exploring Sentence Community for Document-Level Event Extraction. Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2021, Punta Cana, Dominican Republic.10.18653/v1/2021.findings-emnlp.32 ↗
17. Xu, R., Liu, T., Li, L., and Chang, B. (2021, January 1–6). Document-Level Event Extraction via Heterogeneous Graph-Based Interaction Model with a Tracker. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, Online.10.18653/v1/2021.acl-long.274 ↗
18. Nguyen, T.H., Cho, K., and Grishman, R. (2016, January 12–17). Joint Event Extraction via Recurrent Neural Networks. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, San Diego, CA, USA.10.18653/v1/N16-1034 ↗
19. Li, Q., Ji, H., and Huang, L. (2013, January 4–9). Joint Event Extraction via Structured Prediction with Global Features. Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, Sofia, Bulgaria.
20. Nguyen, T.M., and Nguyen, T.H. (February, January 27). One for All: Neural Joint Modeling of Entities and Events. Proceedings of the Thirty-First Innovative Applications of Artificial Intelligence Conference, Honolulu, HI, USA.
21. Sha, L., Qian, F., Chang, B., and Sui, Z. (2018, January 2–7). Jointly Extracting Event Triggers and Arguments by Dependency-Bridge RNN and Tensor-Based Argument Interaction. Proceedings of the AAAI Conference on Artificial Intelligence, New Orleans, LA, USA.10.1609/aaai.v32i1.12034 ↗
22. Liu, X., Luo, Z., and Huang, H. (November, January 31). Jointly Multiple Events Extraction via Attention-Based Graph Information Aggregation. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium.10.18653/v1/D18-1156 ↗
23. Wadden, D., Wennberg, U., Luan, Y., and Hajishirzi, H. (2019, January 3–7). Entity, Relation, and Event Extraction with Contextualized Span Representations. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, Hong Kong, China.10.18653/v1/D19-1585 ↗
24. Li, F., Peng, W., Chen, Y., Wang, Q., Pan, L., Lyu, Y., and Zhu, Y. (2020, January 16–20). Event Extraction as Multi-Turn Question Answering. Proceedings of the Findings of the Association for Computational Linguistics, Online.10.18653/v1/2020.findings-emnlp.73 ↗
25. Du, X., and Cardie, C. (2020, January 16–20). Event Extraction by Answering (Almost) Natural Questions. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, Online.10.18653/v1/2020.emnlp-main.49 ↗
26. Chen, Y., Chen, T., Ebner, S., White, A.S., and Van Durme, B. (2020, January 20). Reading the Manual: Event Extraction as Definition Comprehension. Proceedings of the Fourth Workshop on Structured Prediction for NLP, Online.10.18653/v1/2020.spnlp-1.9 ↗
27. Yang, S., Feng, D., Qiao, L., Kan, Z., and Li, D. (August, January 28). Exploring Pre-Trained Language Models for Event Extraction and Generation. Proceedings of the 57th Conference of the Association for Computational Linguistics, Florence, Italy.
28. Xu, N., Xie, H., and Zhao, D. (November, January 30). A Novel Joint Framework for Multiple Chinese Events Extraction. Proceedings of the Chinese Computational Linguistics-19th China National Conference, Haikou, China.
29. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017, January 4–9). Attention Is All You Need. Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, Long Beach, CA, USA.
30. The Hungarian Method for the Assignment Problem10.1002/nav.3800020109 ↗