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FCJM: A Functional Channel Joint Model for Chinese Text Classification Task

Chinese text classification is one of the important branches of Chinese natural language processing and an important downstream task in the field of Chinese natural language processing. It is widely used in specific tasks such as sentiment analysis, spam identification, and public opinion analysis. The traditional Chinese text classification model only selects one of pinyin encoding and text encoding for the processing of Chinese characters. Therefore, this paper designs a separate pinyin encoder to extract the pronunciation features of Chinese characters, and forms a Functional Channel Joint Model (FCJM) with TextCNN model. The experimental results show that the accuracy and F1 value of the FCJM are improved compared to TextRNN, TextRNN_Att, TextRCNN, DPCNN, and Transformer.