# Deep Neural Network Based Relation Extraction: An Overview arXiv: https://arxiv.org/abs/2101.01907 ยท HTML: https://mlsanity.com/paper/2101.01907/ ## Key facts - Cited by 11 papers in the map (data through 14 Oct 2026). - Builds on 31 of its 111 references that are in the map. - First posted on arXiv 6 Jan 2021. ## Builds on (most cited first, 30 of 31) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019) - [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013) - [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014) - [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014) - [FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation](https://mlsanity.com/paper/1810.10147/) (2018) - [A Convolutional Neural Network for Modelling Sentences](https://mlsanity.com/paper/1404.2188/) (2014) - [End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures](https://mlsanity.com/paper/1601.00770/) (2016) - [Graph Convolution over Pruned Dependency Trees Improves Relation Extraction](https://mlsanity.com/paper/1809.10185/) (2018) - [Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme](https://mlsanity.com/paper/1706.05075/) (2017) - [FewRel 2.0: Towards More Challenging Few-Shot Relation Classification](https://mlsanity.com/paper/1910.07124/) (2019) - [Attention Guided Graph Convolutional Networks for Relation Extraction](https://mlsanity.com/paper/1906.07510/) (2019) - [SemEval-2010 Task 8: Multi-Way Classification of Semantic Relations Between Pairs of Nominals](https://mlsanity.com/paper/1911.10422/) (2019) - [More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction](https://mlsanity.com/paper/2004.03186/) (2020) - [Inter-sentence Relation Extraction with Document-level Graph Convolutional Neural Network](https://mlsanity.com/paper/1906.04684/) (2019) - [RESIDE: Improving Distantly-Supervised Neural Relation Extraction using Side Information](https://mlsanity.com/paper/1812.04361/) (2018) - [Relation Classification via Recurrent Neural Network](https://mlsanity.com/paper/1508.01006/) (2015) - [Relation Extraction : A Survey](https://mlsanity.com/paper/1712.05191/) (2017) - [Classifying Relations via Long Short Term Memory Networks along Shortest Dependency Path](https://mlsanity.com/paper/1508.03720/) (2015) - [Robust Distant Supervision Relation Extraction via Deep Reinforcement Learning](https://mlsanity.com/paper/1805.09927/) (2018) - [Classifying Relations by Ranking with Convolutional Neural Networks](https://mlsanity.com/paper/1504.06580/) (2015) - [Improved Relation Classification by Deep Recurrent Neural Networks with Data Augmentation](https://mlsanity.com/paper/1601.03651/) (2016) - [DSGAN: Generative Adversarial Training for Distant Supervision Relation Extraction](https://mlsanity.com/paper/1805.09929/) (2018) - [A Survey of Deep Learning Methods for Relation Extraction](https://mlsanity.com/paper/1705.03645/) (2017) - [Semantic Relation Classification via Bidirectional LSTM Networks with Entity-aware Attention using Latent Entity Typing](https://mlsanity.com/paper/1901.08163/) (2019) - [Semantic Relation Classification via Convolutional Neural Networks with Simple Negative Sampling](https://mlsanity.com/paper/1506.07650/) (2015) - [Attention-Based Capsule Networks with Dynamic Routing for Relation Extraction](https://mlsanity.com/paper/1812.11321/) (2018) - [Multi-Level Structured Self-Attentions for Distantly Supervised Relation Extraction](https://mlsanity.com/paper/1809.00699/) (2018) - [SIRIUS-LTG-UiO at SemEval-2018 Task 7: Convolutional Neural Networks with Shortest Dependency Paths for Semantic Relation Extraction and Classification in Scientific Papers](https://mlsanity.com/paper/1804.08887/) (2018) - [Jointly Extracting Relations with Class Ties via Effective Deep Ranking](https://mlsanity.com/paper/1612.07602/) (2016) ## Built on by (most cited first, 11 of 11) - [KG-FIT: Knowledge Graph Fine-Tuning Upon Open-World Knowledge](https://mlsanity.com/paper/2405.16412/) (2024) - [Information Extraction in Low-Resource Scenarios: Survey and Perspective](https://mlsanity.com/paper/2202.08063/) (2022) - [Clinical Relation Extraction Using Transformer-based Models](https://mlsanity.com/paper/2107.08957/) (2021) - [Enhancing Language Models for Financial Relation Extraction with Named Entities and Part-of-Speech](https://mlsanity.com/paper/2405.06665/) (2024) - [NL2GDPR: Automatically Develop GDPR Compliant Android Application Features from Natural Language](https://mlsanity.com/paper/2208.13361/) (2022) - [Gradient Imitation Reinforcement Learning for General Low-Resource Information Extraction](https://mlsanity.com/paper/2211.06014/) (2022) - [Relational Extraction on Wikipedia Tables using Convolutional and Memory Networks](https://mlsanity.com/paper/2307.05827/) (2023) - [Comparative Analysis of Contextual Relation Extraction based on Deep Learning Models](https://mlsanity.com/paper/2309.06814/) (2023) - [Distantly Supervised Morpho-Syntactic Model for Relation Extraction](https://mlsanity.com/paper/2401.10002/) (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) ## Abstract (arXiv) Knowledge is a formal way of understanding the world, providing a human-level cognition and intelligence for the next-generation artificial intelligence (AI). One of the representations of knowledge is semantic relations between entities. An effective way to automatically acquire this important knowledge, called Relation Extraction (RE), a sub-task of information extraction, plays a vital role in Natural Language Processing (NLP). Its purpose is to identify semantic relations between entities from natural language text. To date, there are several studies for RE in previous works, which have documented these techniques based on Deep Neural Networks (DNNs) become a prevailing technique in this research. Especially, the supervised and distant supervision methods based on DNNs are the most popular and reliable solutions for RE. This article 1) introduces some general concepts, and further 2) gives a comprehensive overview of DNNs in RE from two points of view: supervised RE, which attempts to improve the standard RE systems, and distant supervision RE, which adopts DNNs to design sentence encoder and de-noise method. We further 3) cover some novel methods and recent trends as well as discuss possible future research directions for this task.