改进交叉证据推理图表用于文档级关系提取
Qiankun Pi1,2, Jicang Lu1,2, Taojie Zhu1,2
1PLA Strategic Support Force Information Engineering University, Zhengzhou, Henan, China.
PeerJ. Computer science
|July 10, 2024
概括
本研究引入了一种新的文档级关系提取 (RE) 模型,使用增强交叉证据推理图 (ECRG). 该模型通过预提取关键证据句子和构建图表来捕捉实体关系来提高性能.
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:
- 文档级关系提取 (RE) 旨在识别文档中实体之间的语义连接.
- 现有的方法经常在与无关的句子中的噪音信息作斗争.
- 实体经常分布在多个句子中,需要跨句子关系预测.
研究的目的:
- 开发一种新的文档级RE模型,通过有效利用证据句子来提高性能.
- 通过预先选择相关证据来解决文档级RE中噪音信息的挑战.
- 改进语义信息的挖掘和文档中的实体之间的远程交互.
主要方法:
- 开发了一种利用增强交叉证据推理图 (ECRG) 的文档级RE模型.
- 基于中句的证据提取规则旨在预先提取高质量的证据.
- 证据图表被构建为挖掘内证据提及连接,实体级图表汇总了对实体间远程相互作用的提及.
主要成果:
- 拟议的ECRG模型在文档级关系提取任务上表现得更好.
- 使用中文规则预提取证据句子有效减少了噪音信息.
- 基于图形的方法成功地捕获了本地和远程实体交互.
结论:
- ECRG模型提供了一种更有效的方法来提取文档级别的关系.
- 利用证据图和实体级图增强了挖掘复杂语义关系的能力.
- 这些发现表明,战略证据选择和图形构造对于强大的可再生能源至关重要.
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