Extracting relational triples (subject, predicate, object) from text enables
the transformation of unstructured text data into structured knowledge. The
named entity recognition (NER) and the relation extraction (RE) are two
foundational subtasks in this knowledge generation pipeline. The integration of
subtasks poses a considerable challenge due to their disparate nature. This
paper presents a novel approach that converts the triple extraction task into a
graph labeling problem, capitalizing on the structural information of
dependency parsing and graph recursive neural networks (GRNNs). To integrate
subtasks, this paper proposes a dynamic feedback forest algorithm that connects
the representations of subtasks by inference operations during model training.
Experimental results demonstrate the effectiveness of the proposed method.

从文本中提取关系三元组（主语、谓语、宾语）可以将非结构化文本数据转换为结构化知识。本文提出了一种将三元组提取任务转换为图标签问题的新方法，利用依存解析和图递归神经网络（GRNNs）的结构信息。为了集成子任务，本文提出了一种动态反馈森林算法，通过推理操作在模型训练过程中连接子任务的表示。实验结果证明了该方法的有效性。