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Accurate Named Entity Recognition (NER) is crucial for various informationretrieval tasks in industry. However, despite significant progress intraditional NER methods, the extraction of Complex Named Entities remains arelatively unexplored area. In this paper, we propose a novel system thatcombines object detection for Document Layout Analysis (DLA) with weaklysupervised learning to address the challenge of extracting discontinuouscomplex named entities in legal documents. Notably, to the best of ourknowledge, this is the first work to apply weak supervision to DLA. Ourexperimental results show that the model trained solely on pseudo labelsoutperforms the supervised baseline when gold-standard data is limited,highlighting the effectiveness of our proposed approach in reducing thedependency on annotated data.

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