Abstract
To solve the problem that a single Bidirectional Long Short-Term Memory (BILSTM) model can only learn the features of a specific dimension, and the local dependence between characters is easily lost when character-level segmentation is used to extract semantic features, this paper proposes a Chinese medical named entity recognition method based on multi-segmentation and multi-layer BILSTM. The purpose of multi-word segmentation is to enrich the semantic features that can be used in the model learning process, so as to further improve the entity recognition ability of the model. Multi-layer bidirectional long short-term memory can obtain feature information of different dimensions by setting hidden layers of different sizes, and use the attention mechanism to capture global information. Experimental results show that this method can significantly improve the performance of Chinese medical named entity recognition, and obtain 78.69% F value in real electronic medical record dataset.