Chinese short news text classification based on BERT and sparse autoencoder

Abstract

Short news text classification plays an import role in natural language processing as the popularity of mobile phones. In this paper we propose a Chinese short news text classification method based on BERT and sparse autoencoder, regarding the overfitting caused by pretrained BERT. We use the BERT for text representation, the output vectors of BERT are dimension reduced through the sparse autoencoder, and then the Softmax classifier takes the reduced vectors as input to get the prediction of the input text. Experimental results show that our method mitigate the unbalance of the performance of different categories, raises the overall classification performance by six percentage, effectively alleviates the overfitting of text representation of BERT, and achieve a better Chinese short text classification performance than using naïve autoencoder and without autoencoder.