  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.
