基于语义图提示学习的篇章事件抽取框架

doi:10.3772/j.issn.1000-0135.2026.03.009

情报学报

2026, Vol. 45

Issue (3): 433-446 DOI: 10.3772/j.issn.1000-0135.2026.03.009

情报技术与应用

本期目录 | 过刊浏览 | 高级检索

基于语义图提示学习的篇章事件抽取框架

余传明, 成伟

中南财经政法大学信息工程学院，武汉 430073

Framework for Document-level Event Extraction Based on Semantic Graph Prompt Learning

Yu Chuanming, Cheng Wei

School of Information Engineering, Zhongnan University of Economics and Law, Wuhan 430073

摘要
图/表
参考文献
相关文章 (0)

全文: PDF (3364 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要为解决现有模型主要聚焦于句子级的语义抽象，难以充分发挥AMR（abstract meaning representation）语义结构信息以及语义依赖关系优势的问题，本文提出一种基于语义图提示的篇章事件抽取框架（semantic graph prompt learning for document-level event extraction，SeGPL），通过引入多粒度AMR融合机制和自适应的图提示特征，有效增强了模型的语义建模能力。与最优基线模型TSAR（two-stream AMR-enhanced extraction model）相比，在WikiEvents数据集上，SeGPL在论元识别和论元分类任务中的head F1分数分别提升了2.16和3.36个百分点；在RAMS（richly annotated multilingual schema-guided event structure）数据集中，span F1分数和head F1分数分别提升了0.41和1.13个百分点，为解决长文本中的事件信息抽取提供了参考。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	余传明
	成伟

关键词 ：篇章级事件抽取, 抽象语义表示, 图提示学习, 多粒度信息融合

收稿日期: 2025-03-11

基金资助:国家自然科学基金面上项目“基于知识增强的科技文献创新识别与评价模型研究”（72374219），“危机情境下网络信息传播失序识别与干预方法研究”（72174153）。

作者简介: 余传明，1978年生，博士，教授，博士生导师，主要研究方向为智能信息处理、科技情报，E-mail：yucm@zuel.edu.cn；成伟，1999年生，博士研究生，主要研究方向为数据挖掘、知识管理与知识工程；

引用本文:

余传明, 成伟. 基于语义图提示学习的篇章事件抽取框架[J]. 情报学报, 2026, 45(3): 433-446.
Yu Chuanming, Cheng Wei. Framework for Document-level Event Extraction Based on Semantic Graph Prompt Learning. 情报学报, 2026, 45(3): 433-446.

链接本文:

https://qbxb.istic.ac.cn/CN/10.3772/j.issn.1000-0135.2026.03.009 或 https://qbxb.istic.ac.cn/CN/Y2026/V45/I3/433

1 喻雪寒, 何琳, 王献琪. 基于机器阅读理解的古文事件抽取研究[J]. 情报学报, 2023, 42(3): 316-326.
2 Han R J, Hsu I H, Sun J, et al. ESTER: a machine reading comprehension dataset for reasoning about event semantic relations[C]// Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. Stroudsburg: Association for Computational Linguistics, 2021: 7543-7559.
3 Li F Y, Peng W H, Chen Y G, et al. Event extraction as multi-turn question answering[C]// Findings of the Association for Computational Linguistics: EMNLP 2020. Stroudsburg: Association for Computational Linguistics, 2020: 829-838.
4 Lu D, Ran S H, Tetreault J, et al. Event extraction as question generation and answering[C]// Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2023: 1666-1688.
5 徐浩, 葛琳琳, 张焱, 等. 面向辅助决策的领域知识图谱构建及其场景式应用研究[J]. 科技情报研究, 2025, 7(4): 58-69.
6 Du X Y, Li S, Ji H. Dynamic global memory for document-level argument extraction[C]// Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2022: 5264-5275.
7 叶光辉, 谭启韬, 武川, 等. 融合对比学习的多阶段文献推荐双塔模型[J]. 情报学报, 2025, 44(7): 859-868.
8 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: Association for Computational Linguistics, 2014: 1846-1851.
9 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. Stroudsburg: Association for Computational Linguistics, 2015: 167-176.
10 尹浩然, 曹金璇, 曹鲁喆, 等. 扩充语义维度的BiGRU-AM突发事件要素识别研究[J]. 数据分析与知识发现, 2020, 4(9): 91-99.
11 郭嘉梁, 王巍, 剧京, 等. 融合混合提示与位置感知的突发事件抽取模型[J]. 计算机应用研究, 2025, 42(6): 1771-1777.
12 陆伟, 冯子琨, 程齐凯, 等. 基于先验提示模板的冲突事件抽取方法研究[J]. 情报科学, 2024, 42(4): 1-8.
13 斯彬洲, 孙海春, 吴越. 基于大语言模型和事件融合的电信诈骗事件风险分析[J]. 数据分析与知识发现, 2025, 9(7): 38-51.
14 Bhopale A P, Tiwari A. Transformer based contextual text representation framework for intelligent information retrieval[J]. Expert Systems with Applications, 2024, 238: 121629.
15 Liu W L, Zhou L, Zeng D Y, et al. Beyond single-event extraction: towards efficient document-level multi-event argument extraction[C]// Findings of the Association for Computational Linguistics: ACL 2024. Stroudsburg: Association for Computational Linguistics, 2024: 9470-9487.
16 Bao X Y, Wang Z Q, Gu J H, et al. Revisiting classical Chinese event extraction with ancient literature information[C]// Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2025: 8440-8451.
17 曾金, 江长江, 李新来, 等. 突发事件抽取与演化关系研究——以“应急服务网”为例[J]. 情报学报, 2024, 43(11): 1334-1348.
18 Yang B S, Mitchell T M. Joint extraction of events and entities within a document context[C]// Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Stroudsburg: Association for Computational Linguistics, 2016: 289-299.
19 Yang H, Chen Y B, Liu K, et al. DCFEE: a document-level Chinese financial event extraction system based on automatically labeled training data[C]// Proceedings of ACL 2018, System Demonstrations. Stroudsburg: Association for Computational Linguistics, 2018: 50-55.
20 Xu R X, Liu T Y, Li L, et al. Document-level event extraction via heterogeneous graph-based interaction model with a tracker[C]// Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing. Stroudsburg: Association for Computational Linguistics, 2021: 3533-3546.
21 Huang K H, Peng N Y. Document-level event extraction with efficient end-to-end learning of cross-event dependencies[C]// Proceedings of the Third Workshop on Narrative Understanding. Stroudsburg: Association for Computational Linguistics, 2021: 36-47.
22 Ma Y B, Wang Z H, Cao Y X, et al. Prompt for extraction? PAIE: prompting argument interaction for event argument extraction[C]// Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2022: 6759-6774.
23 Hu R J, Li J, Liu H Y, et al. Knowledge interaction graph guided prompting for event causality identification[J]. Applied Intelligence, 2025, 55(2): Article No.159.
24 Peng J R, Yang W Z, Wei F Y, et al. Prompt for extraction: multiple templates choice model for event extraction[J]. Knowledge-Based Systems, 2024, 289: 111544.
25 Xu R X, Wang P Y, Liu T Y, et al. A two-stream AMR-enhanced model for document-level event argument extraction[C]// Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Stroudsburg: Association for Computational Linguistics, 2022: 5025-5036.
26 Yang Y Q, Guo Q P, Hu X K, et al. An AMR-based link prediction approach for document-level event argument extraction[C]// Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2023: 12876-12889.
27 Hang T T, Liu S T, Feng J, et al. Few-shot relation extraction based on prompt learning: a taxonomy, survey, challenges and future directions[J]. ACM Computing Surveys, 2026, 58(2): Article No.40.
28 Petroni F, Rockt?schel T, Riedel S, et al. Language models as knowledge bases?[C]// Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing. Stroudsburg: Association for Computational Linguistics, 2019: 2463-2473.
29 Brown T B, Mann B, Ryder N, et al. Language models are few-shot learners[C]// Proceedings of the 34th International Conference on Neural Information Processing Systems. Red Hook: Curran Associates, 2020: 1877-1901.
30 Gui J Y, Zhou Y H, Yu K, et al. PSC-BERT: a spam identification and classification algorithm via prompt learning and spell check[J]. Knowledge-Based Systems, 2024, 301: 112266.
31 Qiu Y N, Wang A D, Li C, et al. STEPS: sequential probability tensor estimation for text-to-image hard prompt search[C]// Proceedings of the 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2025: 28640-28650.
32 Ghorbanpour F, Hangya V, Fraser A. Fine-grained transfer learning for harmful content detection through label-specific soft prompt tuning[C]// Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies. Stroudsburg: Association for Computational Linguistics, 2025: 11047-11061.
33 Mai C C, Chen Y, Gong Z Y, et al. PromptCNER: a segmentation-based method for few-shot Chinese NER with prompt-tuning[J]. ACM Transactions on Asian and Low-Resource Language Information Processing, 2025, 24(9): Article No.95.
34 Xu J H, Yang C, Kang X J. LAAP: learning the argument of an entity with event prompts for document-level event extraction[J]. Neurocomputing, 2025, 613: 128584.
35 Chen L Y, Liu J, Duan Y T, et al. KG-prompt: interpretable knowledge graph prompt for pre-trained language models[J]. Knowledge-Based Systems, 2025, 311: 113118.
36 Le M, Luu T N, The A N, et al. Adaptive prompting for continual relation extraction: a within-task variance perspective[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2025, 39(23): 24384-24392.
37 Sun X G, Cheng H, Li J, et al. All in one: multi-task prompting for graph neural networks[C]// Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York: ACM Press, 2023: 2120-2131.
38 Li S, Ji H, Han J W. Document-level event argument extraction by conditional generation[C]// Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Stroudsburg: Association for Computational Linguistics, 2021: 894-908.
39 Ebner S, Xia P, Culkin R, et al. Multi-sentence argument linking[C]// Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2020: 8057-8077.
40 Shi P, Lin J. Simple BERT models for relation extraction and semantic role labeling[PP/OL]. V1. arXiv (2019-04-10). https://arxiv.org/pdf/1904.05255.
41 Lin Y, Ji H, Huang F, et al. A joint neural model for information extraction with global features[C]// Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2020: 7999-8009.
42 Zeng Q, Zhan Q S, Ji H. EA2E: improving consistency with event awareness for document-level argument extraction[C]// Findings of the Association for Computational Linguistics: NAACL 2022. Stroudsburg: Association for Computational Linguistics, 2022: 2649-2655.
43 Du X Y, Cardie C. Event extraction by answering (almost) natural questions[C]// Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing. Stroudsburg: Association for Computational Linguistics, 2020: 671-683.
44 Zhu M N, Xu Z J, Zeng K S, et al. CMNEE: a large-scale document-level event extraction dataset based on open-source Chinese military news[C]// Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation. Stroudsburg: Association for Computational Linguistics, 2024: 3367-3379.