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

arXiv: https://arxiv.org/abs/2305.05836 · HTML: https://mlsanity.com/paper/2305.05836/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 May 2023.

## Built on by (most cited first, 2 of 2)

- [PromptRE: Weakly-Supervised Document-Level Relation Extraction via Prompting-Based Data Programming](https://mlsanity.com/paper/2310.09265/) (2023)
- [Tabular PDF Information Extraction with Local LLMs and Layout-Aware Parsing: A Reliability Evaluation](https://mlsanity.com/paper/2604.00003/) (2026)

## Abstract (arXiv)

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.