知识驱动的跨文档关系提取知识驱动的跨文档关系提取
Monika Jain1, Raghava Mutharaju1, Kuldeep Singh2
1Knowledgeable Computing and Reasoning Lab, IIIT-Delhi, India.
概括
本研究介绍了KXDocRE,这是一种采用域知识的跨文档关系提取 (CrossDocRE) 的新方法. 这种方法提高了性能,并提供了可解释的关系预测.
科学领域:
- 自然语言处理自然语言处理.
- 人工智能的人工智能
- 提取信息 提取信息
背景情况:
- 关系提取 (RE) 通常是一个句子或文档级别的任务.
- 跨文档关系提取 (CrossDocRE) 将RE扩展到多个文档,由于不同的主题,这带来了独特的挑战.
- 现有的CrossDocRE方法忽视了领域知识的关键作用.
研究的目的:
- 提出KXDocRE,这是CrossDocRE的一个新的框架,集成了领域知识.
- 为了提高跨文档关系提取的性能和可解释性.
- 通过结合外部知识来解决当前CrossDocRE方法的局限性.
主要方法:
- 开发了KXDocRE,这是一个嵌入实体域知识直接与输入文本的框架.
- 利用一种新的方法将结构化领域知识集成到CrossDocRE过程中.
- 创建解释文本以解释预测关系的实施方法.
主要成果:
- 与现有的基线方法相比,KXDocRE在CrossDocRE中表现出更好的性能.
- 该框架成功地结合了域名知识,提高了关系提取的准确性.
- 该方法提供可解释的输出,解释预测关系的基础.
结论:
- 整合领域知识对于推进跨文档关系提取至关重要.
- 对于CrossDocRE任务,KXDocRE在性能和可解释性方面都提供了显著的改进.
- 拟议的框架为更复杂,更有知识的信息提取系统铺平了道路.
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