一个改进的基于GPT2的联合事件提取方法,具有位置扩展和知识增强
Tonghui An1,2, Zhenling Zhang1,2, Yangli Jia3,4
1School of Computer Science, Liaocheng University, Liaocheng, 252059, China.
Scientific reports
|November 11, 2025
概括
一个新的模型,PosEKE-GPT2,通过将其视为文本生成任务来改进从大型数据集中提取金融事件. 这种方法增强了从复杂的金融文本中检索信息的能力.
科学领域:
- 自然语言处理自然语言处理.
- 信息检索 信息检索
- 人工智能的人工智能
背景情况:
- 互联网和社交媒体的普及产生了大量的非结构化数据,增加了对有效信息提取的需求.
- 金融文本存在独特的挑战,包括长度,冗余性和复杂性,阻碍了传统事件提取方法.
研究的目的:
- 开发一种先进的金融事件提取模型,克服现有方法的局限性.
- 提高在财务文件中识别事件类型,触发因素和论据的准确性和稳定性.
主要方法:
- 拟议的PosEKE-GPT2,一个基于GPT2的模型,将事件提取作为文本生成任务重构.
- 使用结构化正典文本和子任务提取实现了事件类型,触发器和参数的联合识别策略.
- 引入了扩展的位置编码机制,以更好地表示长文本,以及用于动态外部知识集成的知识增强模块.
主要成果:
- 在DuEE-Fin数据集上,PosEKE-GPT2的F1平均得分为90.61,在FewFC数据集上达到88.85.
- 该模型在金融事件提取任务中显著优于基线模型.
- 废除研究证实了位置编码和知识增强模块的有效性.
结论:
- 在金融事件提取方面,PosEKE-GPT2表现出卓越的性能和稳定性.
- 提出的方法,包括扩展的位置编码和知识增强,对于处理复杂的财务数据是有效的.
- 该模型非常适合于现实世界的金融信息检索应用程序.
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