REKnow: Enhanced Knowledge for Joint Entity and Relation Extraction
This work addresses the problem of extracting relational facts from text for NLP applications, offering a novel approach but with incremental improvements in performance.
The authors tackled the lack of a unified framework and effective use of external knowledge in relation extraction by proposing a knowledge-enhanced generative model that sequentially generates relational triplets, achieving superior performance on benchmarks like WebNLG, NYT10, and TACRED.
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.