mlsanityData to 14 Oct 2026

REKnow: Enhanced Knowledge for Joint Entity and Relation Extraction

arXiv 2206.05123 ↗First posted 10 Jun 2022cs.CL cs.IRcited by 3

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Abstract (arXiv)

Relation extraction is an important but challenging task that aims to extract all hidden relational facts from the text. With the development of deep language models, relation extraction methods have achieved good performance on various benchmarks. However, we observe two shortcomings of previous methods: first, there is no unified framework that works well under various relation extraction settings; second, effectively utilizing external knowledge as background information is absent. In this work, we propose a knowledge-enhanced generative model to mitigate these two issues. Our generative model is a unified framework to sequentially generate relational triplets under various relation extraction settings and explicitly utilizes relevant knowledge from Knowledge Graph (KG) to resolve ambiguities. Our model achieves superior performance on multiple benchmarks and settings, including WebNLG, NYT10, and TACRED.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 43 of its 64 references that are in the map. First posted on arXiv 10 Jun 2022.

Builds on

43 of 64 references.

References · 43

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31 more
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Cited by year

Citing papers per year: 2022: 2; 2023: 0; 2024: 0; 2025: 1.

3 papers.

  1. 2022Sequence Generation with Label Augmentation for Relation Extractioncited by 9
  2. 2022Collaborative Knowledge Graph Fusion by Exploiting the Open Corpuscited by 0
  3. 2025DISCIE -- Discriminative Closed Information Extractioncited by 0