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.

翻译：信息抽取是自然语言处理中的重要任务，涉及从非结构化文本中提取命名实体及其关系。本文通过将该任务建模为图结构学习，提出了一种新颖方法。通过将信息抽取转化为图结构学习，我们增强了模型在抽取过程中动态优化图结构的能力。与先前对实体和关系进行分离或非联合预测的模型不同，这种建模方式使得模型能够在实体和关系预测中实现更好的交互和结构感知决策。在联合实体关系抽取基准测试中，与最先进基线模型相比，我们的模型GraphER取得了具有竞争力的结果。