Joint Entity and Relation Extraction Model Based on Inner and Outer Tensor Dot Product and Single-Table Filling

Abstract

Joint relational triple extraction is a crucial step in constructing a knowledge graph from unstructured text. Recently, multiple methods have been proposed for extracting relationship triplets. Notably, end-to-end table-filling methods have garnered significant research interest due to their efficient extraction capabilities. However, existing approaches usually generate separate tables for each relationship, which neglects the global correlation between relationships and context, producing a large number of useless blank tables. This problem results in issues of redundant information and sample imbalance. To address these challenges, we propose a novel framework for joint entity and relation extraction based on a single-table filling method. This method incorporates all relationships as prompts within the text sequence and associates entity span information with relationship labels. This approach reduces the generation of redundant information and enhances the extraction capability for overlapping triplets. We utilize the internal and external multi-head tensor fusion approach to generate two sets of table feature vectors. These vectors are subsequently merged to capture a wider range of global information. Experimental results on the NYT and WebNLG datasets demonstrate the effectiveness of our proposed model, which maintains excellent performance, even in complex scenarios involving overlapping triplets.

Keywords

• joint entity and relation extraction
• table filling
• deep learning models
• prompt learning
• natural language processing