Research on Construction of Medical English Corpus and Automatic Labeling Algorithm Based on Deep Learning

This inquiry zeroes in on the assembly of a medical English corpus and the formulation of an auto-annotation algorithm, intent on enhancing the precision and effectiveness of medical text scrutiny. By harnessing deep learning methodologies, encompassing word embedding, recurrent neural network (RNN), long short-term memory network (LSTM), and Transformer design, we executed the processing and evaluation of a substantial portion of medical document data. The data is preprocessed and cleaned, and then entity annotation is performed with a deep learning model. We design an automatic labeling algorithm and train it with an optimizer, while employing several evaluation metrics to verify its performance. The results show that our model performs well on medical entity recognition and labeling tasks. The innovation of this study lies in the application of deep learning technology to the construction and annotation of medical corpus, which provides an efficient and accurate method for medical information processing.