Multi-label text classification model integrating two-channel label semantics
A two-channel label semantic enhancement model was proposed for label semantic representation in multi-label text classification tasks.The model comprised two key components:the graph convolutional network module based on label co-occurrence and the label semantic embedding module based on pre-training.The former leveraged graph convolutional network to capture semantic associations among labels,thereby enhancing the semantic information of each label.The latter utilized prior knowledge from pre-trained models to augment the semantic representation of labels.Finally,an attention mechanism was employed to fuse and deeply encode label semantic information from the dual channels.The experimental results of multi-label text classification on two public datasets,AAPD and RCV1-V2,indicate that compared with mainstream baseline methods,our framework demonstrates significant improvements in terms of precision,recall,and micro-F1.
multi-label text classificationlabel semantic embeddingpre-trained language modelgraph convolutional network
万方数据
维普
