Variant bad text recognition based on ChineseBERT-BiGRU

Abstract

The bad text containing variant words seriously harms the health of the network environment. The existing methods for the recognition of bad variant text do not take into account the importance of the phonetic and positional information for the recognition of bad variant text. In this paper, a ChineseBERT-BiGRU variant bad text recognition model is proposed. This model first learns and trains the word vector of the text by inputting the pinyin information, font information and character information of the text, and then combines the position information of the text. Then the word vector of the text is input into BiGRU to learn richer semantic information of the text. Finally, the bad text containing variant words is identified through Softmax classification. The accuracy, accuracy and F1 values of ChineseBERT-BiGRU in the data set in this paper are compared with those of other models.