Agricultural text classification method based on dynamic fusion of multiple features

• Published in: IEEE access 2023-01, Vol.11, p.1-1
• Main Authors: Li, Yancui, Zhang, Shunli, Lai, Chunxiao
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Attention mechanism; Bi-directional Long Short Term Memory network; Classification; Convolution Neural Network; Convolutional neural networks; Diseases; Dynamic fusion of multivariate features; Feature extraction; Long short term memory; Neural networks; Numerical analysis; Numerical models; Numerical value features; Semantics; Text categorization
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2023.3253386

The traditional text classification method, which treats the values in agricultural text as characters, will lose the original semantic expression of numerical features. In order to fully mine the deep latent semantic features in agricultural text, a novel text classification method based on multivariate feature dynamic fusion is proposed. The Bi-directional Long Short Term Memory network (Bi-LSTM) model with attention mechanism was used to extract the global key semantic features of the text; the multiple windows Convolution Neural Network were used to extract the local semantic information of the text at different levels; Numerical value features containing essential semantic expression were extracted by artificial method to construct the numerical value feature vector. By introducing the attention mechanism to dynamically fuse the extracted multiple semantic features, which can further enrich the deep semantic expression of agricultural text and effectively improve the effect of agricultural text classification with phenotypic numerical type.