Research on joint entity relationship extraction based on graph convolutional neural network

Zhen Li; Zengchun Yang; Yinglong Wang

doi:10.1117/12.3026300

8 April 2024 Research on joint entity relationship extraction based on graph convolutional neural network

Zhen Li, Zengchun Yang, Yinglong Wang

Proceedings Volume 13090, International Conference on Computer Application and Information Security (ICCAIS 2023); 130903T (2024) https://doi.org/10.1117/12.3026300
Event: International Conference on Computer Application and Information Security (ICCAIS 2023), 2023, Wuhan, China

Abstract

A parameter sharing model using syntax based graph convolutional neural networks to capture text structure information is proposed to address issues such as error propagation and ignoring inherent relationships between subtasks in pipeline models. This article will specifically introduce a model that combines parameter sharing mode, including the motivation for designing the model, special annotation strategies, and model structure, experimental settings, and analysis of experimental results. Traditional entity relationship extraction was composed of two sub tasks: entity recognition and relationship extraction. When there are errors in entity recognition, the error information will be propagated to the relationship extraction task, leading to error accumulation. In response to this issue, this article proposed a study on joint entity relationship extraction based on graph convolutional neural networks.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhen Li, Zengchun Yang, and Yinglong Wang "Research on joint entity relationship extraction based on graph convolutional neural network", Proc. SPIE 13090, International Conference on Computer Application and Information Security (ICCAIS 2023), 130903T (8 April 2024); https://doi.org/10.1117/12.3026300

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