关系抽取
关系(数据库)
计算机科学
嵌入
相关性
任务(项目管理)
分类器(UML)
语义关系
人工智能
自然语言处理
数据挖掘
机器学习
数学
心理学
认知
几何学
管理
神经科学
经济
作者
Ridong Han,Peng Tao,Benyou Wang,Lu Liu,Wei Xiang
出处
期刊:Cornell University - arXiv
日期:2022-12-20
标识
DOI:10.48550/arxiv.2212.10171
摘要
Document-level relation extraction faces two overlooked challenges: long-tail problem and multi-label problem. Previous work focuses mainly on obtaining better contextual representations for entity pairs, hardly address the above challenges. In this paper, we analyze the co-occurrence correlation of relations, and introduce it into DocRE task for the first time. We argue that the correlations can not only transfer knowledge between data-rich relations and data-scarce ones to assist in the training of tailed relations, but also reflect semantic distance guiding the classifier to identify semantically close relations for multi-label entity pairs. Specifically, we use relation embedding as a medium, and propose two co-occurrence prediction sub-tasks from both coarse- and fine-grained perspectives to capture relation correlations. Finally, the learned correlation-aware embeddings are used to guide the extraction of relational facts. Substantial experiments on two popular DocRE datasets are conducted, and our method achieves superior results compared to baselines. Insightful analysis also demonstrates the potential of relation correlations to address the above challenges.
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