Chinese Medical Short Text Matching Model Based on Fine-Tuning BERT-Attention-BiLSTM
Xuesong Hu, Huajun Zhang, Youjun Sun · 2023
With the increasing scale of the Internet medical market, a large number of platforms have started to provide free medical diagnosis services to users. The number of questions on medical Q&A platforms is growing rapidly, and there are a lot of repetitive and similar questions, which can no longer meet the demand of users to get fast and accurate answers only by the manual answers of medical experts. To solve the above problems, this paper proposes a Chinese medical short text matching model based on fine-tuning BERT-Attention-BiLSTM (FBAB), use fine-tuning BERT model for representation of text, the attention mechanism is used to obtain the interaction information between sentences, use the Bi-directional Long Short-Term Memory (BiLSTM) to fuse the contextual information of the 2 sentences., and the feature information is further extracted by maximum pooling and mean pooling to obtain the sentence-level semantic representation, which is input to the prediction layer after stitching, and the softmax classification function is used to calculate the probability that the two sentences are similar. Experiments show that compared with the classical deep short text matching model, the FBAB model proposed in this paper shows better results on the medical short text corpus, which proves the effectiveness and feasibility of the model.