GraphER: A Structure-aware Text-to-Graph Model for Entity and Relation Extraction
This addresses information extraction for NLP applications, but it appears incremental as it builds on existing graph-based methods.
The paper tackles joint entity and relation extraction by formulating it as graph structure learning, allowing dynamic refinement of graph structures during extraction, and achieves competitive results on benchmarks.
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.