# A Survey of Deep Learning Methods for Relation Extraction

arXiv: https://arxiv.org/abs/1705.03645 · HTML: https://mlsanity.com/paper/1705.03645/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 May 2017.

## Built on by (most cited first, 16 of 16)

- [GatorTron: A Large Clinical Language Model to Unlock Patient Information from Unstructured Electronic Health Records](https://mlsanity.com/paper/2203.03540/) (2022)
- [An End-to-end Model for Entity-level Relation Extraction using Multi-instance Learning](https://mlsanity.com/paper/2102.05980/) (2021)
- [Deep Neural Approaches to Relation Triplets Extraction: A Comprehensive Survey](https://mlsanity.com/paper/2103.16929/) (2021)
- [Deep Neural Network Based Relation Extraction: An Overview](https://mlsanity.com/paper/2101.01907/) (2021)
- [SocAoG: Incremental Graph Parsing for Social Relation Inference in Dialogues](https://mlsanity.com/paper/2106.01006/) (2021)
- [Clinical Relation Extraction Using Transformer-based Models](https://mlsanity.com/paper/2107.08957/) (2021)
- [Learning to Extract Structured Entities Using Language Models](https://mlsanity.com/paper/2402.04437/) (2024)
- [Literature review on vulnerability detection using NLP technology](https://mlsanity.com/paper/2104.11230/) (2021)
- [gBuilder: A Scalable Knowledge Graph Construction System for Unstructured Corpus](https://mlsanity.com/paper/2208.09705/) (2022)
- [Accelerated materials language processing enabled by GPT](https://mlsanity.com/paper/2308.09354/) (2023)
- [Relation Extraction from News Articles (RENA): A Tool for Epidemic Surveillance](https://mlsanity.com/paper/2311.01472/) (2023)
- [Towards Human-Like Machine Comprehension: Few-Shot Relational Learning in Visually-Rich Documents](https://mlsanity.com/paper/2403.15765/) (2024)
- [Adapting PromptORE for Modern History: Information Extraction from Hispanic Monarchy Documents of the XVIth Century](https://mlsanity.com/paper/2406.00027/) (2024)
- [Maximizing Relation Extraction Potential: A Data-Centric Study to Unveil Challenges and Opportunities](https://mlsanity.com/paper/2409.04934/) (2024)
- [Extracting Structured Requirements from Unstructured Building Technical Specifications for Building Information Modeling](https://mlsanity.com/paper/2508.13833/) (2025)
- [Transformer Enhanced Relation Classification: A Comparative Analysis of Contextuality, Data Efficiency and Sequence Complexity](https://mlsanity.com/paper/2509.11374/) (2025)

## Abstract (arXiv)

Relation Extraction is an important sub-task of Information Extraction which has the potential of employing deep learning (DL) models with the creation of large datasets using distant supervision. In this review, we compare the contributions and pitfalls of the various DL models that have been used for the task, to help guide the path ahead.