SGORTE: Supervised Contrastive Learning and Global Feature-Oriented Based Object Detection Framework for Relational Triple Extraction

The Relational Triple Extraction (RTE) is a fundamental and essential task in information extraction and knowledge graph construction. Recently, the table filling RTE methods have attracted more and more attention due to its good performance. However, there are still some problems with this kind of methods, such as only focusing on local features and not making full use of the regional information of triples. To overcome these deficiencies, we propose a Supervised contrastive learning and Global feature-oriented based Object detection framework for Relational Triple Extraction (SGORTE). Specifically, we convert the table filling RTE task into an object detection task, introduce multiple positive examples and a penalty term through a designed supervised contrastive learning method to enhance the robustness of the framework. In addition, we combine vertices-based bounding box detection and global relational region detection to fully utilize the relevant information of the triples for extraction. We conduct extensive experiments on two widely used datasets, and the experimental results show that the proposed framework performs better than the state-of-the-art baselines, and has obvious performance improvements in a variety of complex scenarios.