Research on Multi-label Text Classification Based on Feature Fusion
As an important step in multi-label text classification,currently,significant progress has been made in feature extraction methods.Nevertheless,the obstacles of single and incomplete feature acquisition based on deep learning remain.Therefore,this paper proposes a new feature fusion extraction model,which employs BiGRU to extract global features of text,Capsule network to extract local features and position information of text,and TF-IDF to extract statistical features of text.Experiments have shown that the performance of this model has been improved on both the public dataset RCV1-V2 and AAPD.
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