Entity and Relationship Extraction with High-quality Spans and Enhanced Marker Strategies

Entity and relation extraction is a critical task in information extraction. Recent approaches have emphasized obtaining improved span representations. However, existing work suffers from two major drawbacks. First, there is an overabundance of low-quality candidate spans, which hinders the effective extraction of information from high-quality candidate spans. Second, the information encoded by existing marker strategies is often too simple to fully capture the nuances of the span, resulting in the loss of potentially valuable information. To address these issues, we propose a enhancing entity and relation extraction with high-quality spans and enhanced marker strategies (HSEM), it assigns adaptive weights to different spans in order to make the model more focused on high quality spans. Specifically, the HSEM model enriches marker representation to incorporate more span information and enhance entity categorization. Additionally, we design a span scoring framework that assesses span quality based on the fusion of internal information and focuses the model on training high-quality samples to improve performance. Experimental results on six benchmark datasets demonstrate that our model achieves state-of-the-art results after discriminating span quality.