Generating Chinese Event Extraction Method Based on ChatGPT and Prompt Learning
-
Published:2023-08-22
Issue:17
Volume:13
Page:9500
-
ISSN:2076-3417
-
Container-title:Applied Sciences
-
language:en
-
Short-container-title:Applied Sciences
Author:
Chen Jianxun1, Chen Peng1, Wu Xuxu1
Affiliation:
1. School of Information Network Security, People’s Public Security University of China, Beijing 100038, China
Abstract
Regarding the scarcity of annotated data for existing event extraction tasks and the insufficient semantic mining of event extraction models in the Chinese domain, this paper proposes a generative joint event extraction model to improve existing models in two aspects. Firstly, it utilizes the content generation capability of ChatGPT to generate annotated data corpora for event extraction tasks and trains the model using supervised learning methods adapted to downstream tasks. Secondly, explicit entity markers and event knowledge are added to the text to construct generative input templates, enhancing the performance of event extraction. To validate the performance of this model, experiments are conducted on DuEE1.0 and Title2Event public datasets, and the results show that both data enhancement and prompt learning based on ChatGPT effectively improve the performance of the event extraction model, and the F1 values of the events extracted by the CPEE model proposed in this paper reach 85.1% and 59.9% on the two datasets, respectively, which are comparable to the existing models’ values of 1.3% and 10%, respectively; moreover, on the Title2Event dataset, the performance of different models on the event extraction task can be gradually improved as the data size of the annotated corpus of event extraction generated using ChatGPT increases.
Funder
2022 Post Graduate Course Construction Project of People’s Public Security University of China Fundamental Research Funds for the Central Universities
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference38 articles.
1. A Survey of Event Extraction Methods from Text for Decision Support Systems;Hogenboom;Decis. Support Syst.,2016 2. Garofolo, J. (2021, October 03). Automatic Content Extraction (ACE), Available online: http://itl.gov/iad/mig/-tests/ace/2005. 3. Walker, C., Strassel, S., Medero, J., and Maeda, K. (2006). ACE 2005 Multilingual Training Corpus LDC2006T06, Linguistic Data Consortium. Web Download. 4. Satyapanich, T., Ferraro, F., and Finin, T. (2020, January 7–12). CASIE: Extracting Cybersecurity Event Information from Text. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA. 5. Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., and Zettlemoyer, L. (2020, January 5–10). BART: Denoising Sequence-to-Sequence Pre-Training for Natural Language Generation, Translation, and Comprehension. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Online.
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|