Medical Named Entity Recognition Based on Overlapping Neural Networks

Abstract Named entity recognition (NER) of medical text is a basic task in electronic medical text processing. In recent years, chinese named entity recognition system, especially in the medical field, has problems with insufficient semantic information and poor coding ability. Aiming at the existing problems, we put forward a overlapping neural network for medical named entity recognition. Compared with the mainstream methods for sequence tagging task in recent years, our proposed method can learn context feature automatically and handle encoding process better. The comparative experiments are carried out on medical NER data set, and the experimental results show that our proposed overlapping neural network model can obtain better performance than the state-of-the-art models.