融合多粒度特征和标签语义共现的多标签分类.

In order to analyze the content contained in the text more accurately and provide suggestions for people' s production and life, a multi-label classification model that combines multi-granularity features and label semantics on the basis of the traditional multilabel classification method based on deep learning was proposed. Bidirectional long short-term memory network(Bi-LSTM) was used in this model to extract multi-grained text features and obtain text features at different levels. The label diagram was constructed by calculating pmi, and the graph convolution network(GCN) was used to extract hidden relationships of labels and obtain text representation with label information. Finally, multi-granularity text features were fused to carry out multi-label text classification. Experiments were conducted on AAPD and news data sets. The results show that the Micro-F1 values of the proposed model are up to 0. 704 and 0. 729, respectively, which verifies the effectiveness of the model. [ABSTRACT FROM AUTHOR]