  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. Our
experimental results show that the model trained solely on pseudo labels
outperforms the supervised baseline when gold-standard data is limited,
highlighting the effectiveness of our proposed approach in reducing the
dependency on annotated data.
