Neural relation extraction: a survey
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Abstract (arXiv)
Neural relation extraction discovers semantic relations between entities from unstructured text using deep learning methods. In this study, we present a comprehensive review of methods on neural network based relation extraction. We discuss advantageous and incompetent sides of existing studies and investigate additional research directions and improvement ideas in this field.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 23 Jun 2020.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 2.
4 papers.
- 2021Clinical Relation Extraction Using Transformer-based Modelscited by 9
- 2024Retrieval-Augmented Generation-based Relation Extractioncited by 9
- 2022Pre-trained Language Models as Re-Annotatorscited by 0
- 2024Maximizing Relation Extraction Potential: A Data-Centric Study to Unveil Challenges and Opportunitiescited by 0
- 2024Maximizing Relation Extraction Potential: A Data-Centric Study to Unveil Challenges and Opportunities7 Sep 2024
- 2024Retrieval-Augmented Generation-based Relation Extraction20 Apr 2024
- 2022Pre-trained Language Models as Re-Annotators11 May 2022
- 2021Clinical Relation Extraction Using Transformer-based Models19 Jul 2021