A Comprehensive but Effective Share-Dependent Bidirectional Framework for Relational Triple Extraction

Abstract

In the evolving landscape of information extraction systems, relational triple extraction has emerged as a pivotal component, especially with the rise of tagging-based methods. This research addresses the inherent limitations of traditional unidirectional and subject-centric models, which often struggle with complex relational structures, leading to compromised accuracy. We introduce a bidirectional extraction framework, a significant departure from conventional models. This novel approach diminishes the heavy dependence on subject extraction, thereby enhancing both the accuracy and robustness of the process. At the heart of our method is the parallel identification of subject-object pairs, underpinned by a shared encoder that adeptly merges feature fusion to augment process interdependence. Our model incorporates a relation dual-stream extraction mechanism, integrating a coordinate attention system essential for assigning complex relationships to each entity pair. This is further refined through a dependency encoder, ensuring a nuanced and precise extraction process. The innovation of this approach lies in its dual-stream framework, meticulously designed to handle the nuances of shared and dependency structures in relational data. This strategy successfully addresses the challenges of cross-dependencies and the oversight of inter-dependency elements, common in earlier models. Through extensive evaluations across multiple benchmark datasets, this model demonstrates superior performance compared to existing methodologies. Its versatility suggests a significant potential for advancing other tagging-based relational triple extraction methods. Consequently, this research not only establishes a new benchmark in relational triple extraction but also indicates a promising direction for the development of more sophisticated and accurate information extraction systems. This has broad implications for the future of natural language processing and knowledge graph construction.

Keywords

• Relational triple extraction
• joint extraction of entities and relations
• deep learning