Method for Adaptive Combination of Multiple Features for Text Classification in Agriculture

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When applying conventional text classification techniques, the values in agricultural text are converted into characters, which destroys the original semantic representation of numerical aspects. A unique text classification approach is suggested, based on the dynamic fusion of several characteristics, to completely mine the deep latent semantic characteristics in agricultural literature. The global key semantic characteristics of the text were extracted using the Bi-directional Gated Recurrent Neural Networks (GRU) model with attention mechanism, while the local semantic data about the text at various levels was extracted using the multiple windows Convolution Neural Network. Finally, the number that features essential semantic expressions was obtained using a machine learning approach for creating the quantitative value feature vector. To further enhance the deep semantic expression found in agricultural text and successfully improve the impact of farm text categorization with phenotypic numerical type, we use a focus technique to dynamically fuse the derived numerous semantic characteristics.