mlsanityData to 7 Oct 2026

Deep Neural Network Based Relation Extraction: An Overview

arXiv 2101.01907 ↗First posted 6 Jan 2021cs.CL cs.AIcited by 11

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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.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 31 of its 111 references that are in the map. First posted on arXiv 6 Jan 2021.

Builds on

31 of 111 references.

References · 31

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  6. 2018FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluationcited by 254
  7. 2014A Convolutional Neural Network for Modelling Sentencescited by 163
  8. 2016End-to-End Relation Extraction using LSTMs on Sequences and Tree Structurescited by 161
  9. 2018Graph Convolution over Pruned Dependency Trees Improves Relation Extractioncited by 146
  10. 2017Joint Extraction of Entities and Relations Based on a Novel Tagging Schemecited by 102
  11. 2019FewRel 2.0: Towards More Challenging Few-Shot Relation Classificationcited by 94
  12. 2019Attention Guided Graph Convolutional Networks for Relation Extractioncited by 82
19 more
  1. 2019SemEval-2010 Task 8: Multi-Way Classification of Semantic Relations Between Pairs of Nominalscited by 63
  2. 2020More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extractioncited by 52
  3. 2019Inter-sentence Relation Extraction with Document-level Graph Convolutional Neural Networkcited by 51
  4. 2018RESIDE: Improving Distantly-Supervised Neural Relation Extraction using Side Informationcited by 47
  5. 2015Relation Classification via Recurrent Neural Networkcited by 46
  6. 2017Relation Extraction : A Surveycited by 44
  7. 2015Classifying Relations via Long Short Term Memory Networks along Shortest Dependency Pathcited by 35
  8. 2018Robust Distant Supervision Relation Extraction via Deep Reinforcement Learningcited by 35
  9. 2015Classifying Relations by Ranking with Convolutional Neural Networkscited by 33
  10. 2016Improved Relation Classification by Deep Recurrent Neural Networks with Data Augmentationcited by 26
  11. 2018DSGAN: Generative Adversarial Training for Distant Supervision Relation Extractioncited by 23
  12. 2017A Survey of Deep Learning Methods for Relation Extractioncited by 16
  13. 2019Semantic Relation Classification via Bidirectional LSTM Networks with Entity-aware Attention using Latent Entity Typingcited by 13
  14. 2015Semantic Relation Classification via Convolutional Neural Networks with Simple Negative Samplingcited by 12
  15. 2018Attention-Based Capsule Networks with Dynamic Routing for Relation Extractioncited by 10
  16. 2018Multi-Level Structured Self-Attentions for Distantly Supervised Relation Extractioncited by 8
  17. 2018SIRIUS-LTG-UiO at SemEval-2018 Task 7: Convolutional Neural Networks with Shortest Dependency Paths for Semantic Relation Extraction and Classification in Scientific Paperscited by 3
  18. 2016Jointly Extracting Relations with Class Ties via Effective Deep Rankingcited by 2
  19. 2019Transfer Learning for Relation Extraction via Relation-Gated Adversarial Learningcited by 1

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Cited by year

Citing papers per year: 2021: 1; 2022: 3; 2023: 2; 2024: 5.

11 papers.

  1. 2024KG-FIT: Knowledge Graph Fine-Tuning Upon Open-World Knowledgecited by 16
  2. 2022Information Extraction in Low-Resource Scenarios: Survey and Perspectivecited by 10
  3. 2021Clinical Relation Extraction Using Transformer-based Modelscited by 9
  4. 2024Enhancing Language Models for Financial Relation Extraction with Named Entities and Part-of-Speechcited by 1
  5. 2022NL2GDPR: Automatically Develop GDPR Compliant Android Application Features from Natural Languagecited by 0
  6. 2022Gradient Imitation Reinforcement Learning for General Low-Resource Information Extractioncited by 0
  7. 2023Relational Extraction on Wikipedia Tables using Convolutional and Memory Networkscited by 0
  8. 2023Comparative Analysis of Contextual Relation Extraction based on Deep Learning Modelscited by 0
  9. 2024Distantly Supervised Morpho-Syntactic Model for Relation Extractioncited by 0
  10. 2024Adapting PromptORE for Modern History: Information Extraction from Hispanic Monarchy Documents of the XVIth Centurycited by 0
  11. 2024Maximizing Relation Extraction Potential: A Data-Centric Study to Unveil Challenges and Opportunitiescited by 0