The existing virtual assistants (VAs) for medical services cannot output satisfactory results on Chinese language processing (CLP). This paper attempts to design a VA that identifies the seriousness and improves the awareness of breast cancer (BC) based on inputs of Chinese texts. Our VA was developed based on the neural network called long short-term memory (LSTM), integrating two N-gram models, namely, bigram and trigram. The integrated models are critical to text-based Chinese word segmentation (CWS). The sequence-to-sequence learning was introduced to covert the CWS into a framework of sequence classification. The proposed VA was compared with several state-of-the-art methods through an experiment. The results show that our method achieved a high accuracy (94%~97%) in identifying the high-frequency characters. The research findings are helpful to the BC identification of Chinese women.
virtual assistance, sequence to sequence neural network, bigram and trigram