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Global–Local Enhancement Network for Short Text Classification

Because of the limited context information, it is a challenging task to classify short texts. Most existing methods only focus on extracting high-quality local features or global features from text to construct text representations, which is not comprehensive enough. This article proposes a global–local enhancement network (GLEN), which can construct a high-quality text representation by integrating the global and local features of the text. To improve the quality of global and local features, a group-wise enhancement mechanism is introduced, which can effectively enhance the important features while weakening the unimportant features. For the information-loss problem of traditional pooling operation, we designed a global–local pooling mechanism, which can filter out local features that are more relevant to the whole information of the text. Seven benchmark datasets of text classification were selected to test the performance of the model, and GLEN obtained the best results on most datasets. The ablation experiment on GLEN shows that both the group-wise enhancement mechanism and the global–local pooling mechanism can effectively improve the performance of the model.