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 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.
Abstract (translated)
信息抽取（IE）是自然语言处理（NLP）中一个重要任务，涉及从无结构文本中提取命名实体及其关系。在本文中，我们提出了一种将IE表示为图结构学习（GSL）的新方法。通过将IE表示为GSL，我们增强了模型在提取过程期间动态精炼和优化图结构的能力。这种表示使得实体和关系预测具有更好的交互性和结构指导决策，与之前这些任务具有单独或松散预测的模型相比。与联合实体和关系提取基准上的最先进基线相比，我们的模型GraphER在竞争中取得了竞争力的结果。
URL
https://arxiv.org/abs/2404.12491
