Extracting Complex Named Entities in Legal Documents via Weakly Supervised Object Detection

Understand

Accurate Named Entity Recognition (NER) is crucial for various information retrieval tasks in industry.

• However, despite significant progress in traditional NER methods, the extraction of Complex Named Entities remains a relatively unexplored area.
• In this paper, we propose a novel system that combines object detection for Document Layout Analysis (DLA) with weakly supervised learning to address the challenge of extracting discontinuous complex named entities in legal documents.
• Notably, to the best of our knowledge, this is the first work to apply weak supervision to DLA.