Abstract: Aiming at the problems of single embedding information,lacking of word boundary and text structure information in Chinese medical named entity recognition(NER) model based on character representation,this paper presents a medical named entity recognition model integrating multi-feature embedding.Firstly,the characters are mapped to a fixed-length embedding representation.Secondly,external resources are introduced to construct lexical feature,which can supplement the potential phrase information of characters.Thirdly,according to the characteristics of Chinese pictographs and text sequences,character structure feature and sequence structure feature are introduced,respectively.The convolutional neural networks are used to encode the two structural features to obtain radial-level word embedding and sentence-level word embedding.Finally,the obtained multiple feature embeddings are concatenated and input into the long short-term memory network encoding,and the entity result is output by the CRF layer.Taking the self-built Chinese medical data and the CHIP_2020 data as the datasets,experimental results show that compared with the benchmark models,the proposed model integrating both lexical feature and text structure feature can effectivelyidentify named entities in the medical field.

Key words: Named entity recognition, Chinese medical text, Lexical information, Text structure features, Deep learning