Integrated Extraction of Entities and Relations via Attentive Graph Convolutional Networks

• Published in: Electronics (Basel) 2024-11, Vol.13 (22), p.4373
• Main Authors: Gao, Chuhan, Xu, Guixian, Meng, Yueting
• Format: Article
• Language: English
• Subjects: Analysis; Artificial neural networks; Classification; Computational linguistics; Data mining; Graphical representations; Inference; Labeling; Language processing; Leak detection; Machine translation; Methods; Natural language interfaces; Natural language processing; Neural networks; Safety and security measures; Semantics; Text categorization; Workloads
• ISSN: 2079-9292; 2079-9292
• DOI: 10.3390/electronics13224373

For information security, entity and relation extraction can be applied in sensitive information protection, data leakage detection, and other aspects. The current approaches to entity relation extraction not only ignore the relevance and dependency between name entity recognition and relation extraction but also may result in the cumulative propagation of errors. To solve this problem, it is proposed that an end-to-end joint entity and relation extraction model based on the Attention mechanism and Graph Convolutional Network (GCN) to simultaneously extract named entities and their relationships. The model includes three parts: the detection of entity span, the construction of an entity relation weighted graph, and the inference of entity relation type. Firstly, the detection of entity spans is viewed as a sequence labeling problem, and a multi-feature fusion approach for word embedding representation is designed to calculate all entity spans in a sentence to form an entity span matrix. Secondly, the entity span matrix is employed in the Multi-Head Attention mechanism for constructing the weighted adjacency matrix of the entity relation graph. Finally, for the inference of entity relation type, considering the interaction between entities and relations, the entity span matrix and relation connection matrix are simultaneously fed into the GCN for integrated extraction of entities and relations. Our model is evaluated on the public NYT dataset, attaining a precision of 66.4%, a recall of 63.1%, and an F1 score of 64.7% for joint entity and relation extraction, significantly outperforming other approaches. Experiments demonstrate that the proposed model is helpful for inferring entities and relations, considering the interaction between entities and relations through the Attention mechanism and GCN.