# Deep Residual Learning for Weakly-Supervised Relation Extraction

arXiv: https://arxiv.org/abs/1707.08866 · HTML: https://mlsanity.com/paper/1707.08866/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jul 2017.

## Built on by (most cited first, 7 of 7)

- [Class-Aware Robust Adversarial Training for Object Detection](https://mlsanity.com/paper/2103.16148/) (2021)
- [BERT might be Overkill: A Tiny but Effective Biomedical Entity Linker based on Residual Convolutional Neural Networks](https://mlsanity.com/paper/2109.02237/) (2021)
- [Multi-Task Attentive Residual Networks for Argument Mining](https://mlsanity.com/paper/2102.12227/) (2021)
- [How Fragile is Relation Extraction under Entity Replacements?](https://mlsanity.com/paper/2305.13551/) (2023)
- [PromptRE: Weakly-Supervised Document-Level Relation Extraction via Prompting-Based Data Programming](https://mlsanity.com/paper/2310.09265/) (2023)
- [TTM-RE: Memory-Augmented Document-Level Relation Extraction](https://mlsanity.com/paper/2406.05906/) (2024)
- [A LayoutLMv3-Based Model for Enhanced Relation Extraction in Visually-Rich Documents](https://mlsanity.com/paper/2404.10848/) (2024)

## Abstract (arXiv)

Deep residual learning (ResNet) is a new method for training very deep neural networks using identity map-ping for shortcut connections. ResNet has won the ImageNet ILSVRC 2015 classification task, and achieved state-of-the-art performances in many computer vision tasks. However, the effect of residual learning on noisy natural language processing tasks is still not well understood. In this paper, we design a novel convolutional neural network (CNN) with residual learning, and investigate its impacts on the task of distantly supervised noisy relation extraction. In contradictory to popular beliefs that ResNet only works well for very deep networks, we found that even with 9 layers of CNNs, using identity mapping could significantly improve the performance for distantly-supervised relation extraction.