  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.
