TGIN:使用双相图推理网络进行文档级事件提取
概括
本研究介绍了用于文档级事件提取的双相图推理网络 (TGIN). TGIN通过明确建模关键信息和减少无关实体的噪音来提高精度.
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:
- 文档级事件提取具有挑战性,因为实体分散在多个句子中.
- 现有的方法在隐式信息建模和不相关实体考虑方面扎,导致噪音和效率降低.
- 对实体交互的有效建模对于准确的文档级事件提取至关重要.
研究的目的:
- 提出一种新的双相图推理网络 (TGIN),以改进文档级事件提取.
- 通过明确建模关键信息和减少无关实体影响来解决以前方法的局限性.
- 为了提高从整个文档中提取事件记录的效率和精度.
主要方法:
- 在第一阶段构建一个异质的文档级图表,以捕捉复杂的交互和获取文档意识特征.
- 使用具有注意力机制的关键信息聚合器来明确聚合实体对的关键句子.
- 在第二阶段构建实体级图,使用预测的实体链接作为先前信息来模型相关实体对之间的相互作用.
主要成果:
- 拟议的TGIN框架在ChFinAnn数据集上显示出卓越的性能,用于文档级事件提取.
- 明确汇总关键信息并专注于相关实体对,显著提高了提取精度.
- 两相图推理方法有效地减少了事件提取中的错误传播和噪声.
结论:
- 通过有效解决噪音和效率问题,TGIN方法在文档级事件提取方面取得了重大进展.
- 通过双相图推理网络明确建模关键信息和实体交互,可以实现更高的提取性能.
- 该框架为从非结构化文档中提取复杂事件记录提供了强大的解决方案.
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