提升文档级事件提取:跨文本的整合和相互反
Min Zuo1,2, Jiaqi Li1,2, Di Wu3
1National Engineering Research Centre for Agri-Product Quality Traceability, Beijing Technology and Business University, Beijing 100048, China.
Mathematical biosciences and engineering : MBE
|December 5, 2023
概括
本研究介绍了跨文本整合和相互反 (IATRF) 模型,用于文档级事件提取. IATRF增强了句子中的上下文理解,改善了从长文本中检索信息.
科学领域:
- 自然语言处理自然语言处理.
- 提取信息 提取信息
- 人工智能的人工智能
背景情况:
- 文档级事件提取旨在识别长文本中的事件.
- 现有的方法很难有效地整合跨句子的上下文信息.
- 挑战包括代表远距离文档上下文和分散的事件参数.
研究的目的:
- 提出一种新的文档级事件提取模型,即跨文本整合和相互反 (IATRF).
- 改进包含跨句子的上下文信息的整合.
- 为了提高事件参数的识别,特别是当分散在句子中时.
主要方法:
- 构建一个异质图,以链接文档和实体信息.
- 采用图形卷积网络 (GCN) 进行上下文意识的语义信息获取.
- 使用变压器分类器进行多标签事件类型分类.
- 引入互惠反引证提取策略,用于引证识别.
主要成果:
- 在COSM和ChFinAnn数据集上,IATRF模型表现出比现有方法更好的性能.
- 该模型获得了更高的F1值,证实了它的有效性.
- 成功解决了远距离上下文表示和跨句话论证分散方面的挑战.
结论:
- 拟议的IATRF模型显著推进了文档级事件提取.
- IATRF有效地捕获文档级上下文,并处理分散的事件参数.
- 这种方法为从广泛的文本中提取复杂信息的任务提供了强大的解决方案.
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