Implicit Perspective for Relational Triple Extraction based on Diffusion Model
The author proposes an innovative approach, IPED, for relational triple extraction using an implicit perspective and a block-denoising diffusion model to overcome challenges in explicit tagging methods. Experimental results show superior performance and efficiency compared to state-of-the-art models.
IPED: An Implicit Perspective for Relational Triple Extraction based on Diffusion Model by Jianli Zhao, Changhao Xu, Bin Jiang
IPED proposes an innovative approach for relational triple extraction using an implicit perspective and denoising diffusion model, achieving state-of-the-art performance and efficiency.
A Bi-consolidating Model for Extracting Overlapping Relational Triples from Sentences
A bi-consolidating model is proposed to simultaneously reinforce the local and global semantic features relevant to each relational triple, which is effective for extracting overlapping relational triples from sentences.
Enhancing Relational Triple Extraction from Complex Sentences using a Model Collaboration Approach
A model collaboration framework that integrates a small evaluation model with large language models to improve the recall of relational triple extraction, especially from complex sentences containing multiple triples.
Region-based Table Filling Method for Extracting Relational Triples from Text
A novel region-based table filling method that exploits local spatial dependencies of relational triples to improve entity pair boundary detection and relational triple extraction.