GraphER: A Structure-aware Text-to-Graph Model for Entity and Relation Extraction

arXiv:2404.12491v1 Announce Type: new
Abstract: 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 …

abstract arxiv cs.ai cs.cl extraction graph information information extraction language language processing natural natural language natural language processing nlp novel paper processing relationships text type unstructured