Chinese Medical Entity Annotation Based on Autonomous Learning

Abstract

Named entity annotation means an entity that needs to be labeled in a prediction sequence on a given text sequence. Labeling high-quality medical entities from Chinese medical texts plays an important role in named entity recognition, and construction of medical knowledge graph. Named entity annotation in medical texts is the premise of the full-supervised and semi-supervised named entities recognition. The current mainstream named entity annotation require a lot of manpower on the corpus labeling, which is laborious and time consuming. For medical entities widely distributed in Chinese medical texts, in this paper, we propose a small number of manually labeled medical entities to autonomously learn medical text features, and iteratively generating new labeled entities. The model automatically iterates the annotations from the original medical text collection to be processed and generates a valid medical entity. The autonomously medical entity labeling work makes it easy to label Chinese medical texts. This framework is tested on real Chinese medical records, and the experimental results show that the method can effectively identify the entities, and has certain practical value.