[Objective] This study uses the Label Embedding technique to modify attention mechanism. It learns the task-specific information and generates task-related attention weights, aiming to improve the quality of text representation vectors.[Methods] First, we adopted Multi-level LSTM to extract potential semantic representation of texts. Then, we retrieved the words attracted most attention with different labels to generate attention weights through Label Embedding. Finally, we calculated the text representation vector with task-specific information, which was used to predict text classification.[Results] Compared with the TextCNN, BiGRU, TLSTM, LSTMAtt, and SelfAtt models, performance of the proposed model on multiple datasets was improved by 0.60% to 11.95% (with an overall average of 5.27%). It also had fast convergence speed and low complexity.[Limitations] The experimental datasets and the task-types need to be expanded.[Conclusions] The proposed model can effectively improve the classification results of text semantics, which has much practical value.
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