Extracting Relational Triples Based on Graph Recursive Neural Network via Dynamic Feedback Forest Algorithm

• URL: http://arxiv.org/abs/2308.11411v1
• Date: Tue, 22 Aug 2023 13:00:13 GMT
• Title: Extracting Relational Triples Based on Graph Recursive Neural Network via Dynamic Feedback Forest Algorithm
• Authors: Hongyin Zhu
• Abstract summary: This paper presents a novel approach that converts the triple extraction task into a graph labeling problem. To integrate subtasks, this paper proposes a dynamic feedback forest algorithm that connects the representations of subtasks by inference operations during model training.
• Score: 0.9463895540925061
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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