Improving inference via rich path information and logic rules for document-level relation extraction

Huizhe Su, Shaorong Xie, Hang Yu, Changsen Yuan, Xinzhi Wang, Xiangfeng Luo

Abstract: The task of document-level relation extraction is the classification of the relations between pairs of entities within a document. The identification of relations between inter-sentence entity pairs is one of the main challenges, as there may be no direct connections between the entities. Previous researches have attempted to address this challenge by leveraging path information between entities in the graph to predict their relations. However, these methods ignore the insufficiency of relations information in the existing paths and the absence of connecting paths between entity pairs. In this paper, we propose an effective inference model that enhances inter-sentence reasoning at both the document and global levels. Our model enhances path information by aggregating features from various sources along the logical reasoning paths between entities within each document. Additionally, the model learns relational inference rules from large graphs created from multiple documents and applies these rules to enrich existing relational knowledge. The experimental results indicate that our model outperforms existing models on three widely used benchmark datasets. Moreover, further analysis highlights that our model is especially effective in document-level relation extraction, particularly for inter-sentence relation extraction.