Information extraction (IE) is an important task in Natural Language
Processing (NLP), involving the extraction of named entities and their
relationships from unstructured text. In this paper, we propose a novel
approach to this task by formulating it as graph structure learning (GSL). By
formulating IE as GSL, we enhance the model's ability to dynamically refine and
optimize the graph structure during the extraction process. This formulation
allows for better interaction and structure-informed decisions for entity and
relation prediction, in contrast to previous models that have separate or
untied predictions for these tasks. When compared against state-of-the-art
baselines on joint entity and relation extraction benchmarks, our model,
GraphER, achieves competitive results.

该研究论文提出了一种新颖的方法，将信息提取任务转化为图结构学习，通过动态细化和优化图结构来增强模型在实体和关系预测方面的能力，相比于以前的模型，该方法允许更好的交互和结构驱动决策，同时在联合实体和关系提取基准测试上与最先进的基准模型相比获得了有竞争力的结果。