AGRICULTURAL TEXT CLASSIFICATION METHOD BASED ON DYNAMIC FUSION OF MULTIPLE FEATURES

Abstract

The traditional text classification methods, 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
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.