Text Classification of Multiple Datasets Based on Multidimensional Information

Multiple datasets enable a deep learning model to achieve a wide range of classifications, while the diversity of datasets reduces classification accuracy. To solve this problem, a method based on multidimensional information is proposed. The first dimension is the outputs of multiple models on different datasets. Through this information, we can predict the dataset that may contain the testing samples. The second one is the outputs of multiple models on the same dataset, through which the labels of testing samples can be classified. The third one is the distribution of labels on the same testing sample, which further increases accuracy. Experimental results show that our method achieves the best performance compared to the existing methods while ensuring good scalability.