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A new approach to text feature extraction using BiReGU and capsule networks

Traditional single-model text feature representation methods lack multi-level representation of features and cannot extract global and local information well. To this end, the text uses a two-layer ReGU network (BiReGU) and a capsule network to extract the global semantic and local semantic features of the text respectively, combining attention mechanisms and interaction forms to obtain a more comprehensive multi-level feature representation of the text. In addition, the traditional capsule network dynamic routing algorithm suffers from the problem of noisy capsule interference. This paper proposes to reduce the weight of noisy capsules based on the attention mechanism and Capsule's feature extraction method, thus reducing the interference to subsequent capsules. Comparative experiments are conducted on a generic dataset and the experimental results demonstrate the effectiveness of the proposed method.