Chinese Medical Named Entity Recognition with Label Knowledge
Named entity recognition in the medical field is one of the important research contents of information extraction tasks.Its training data mainly comes from unstructured texts such as clinical trial data,health records,electronic medical records.How-ever,labeling these data requires professionals to spend a lot of manpower,material resources and ime.In the absence of large-scale medical training data,named entity recognition models in the medical field are prone to recognition errors.In order to solve this problem,this paper proposes a Chinese medical named entity recognition method that integrates label knowledge,that is,after obtaining the interpretation of the text label through a professional field dictionary,the text,label and label interpretation are en-coded separately,and the fusion is performed based on an adaptive fusion mechanism,to effectively balance the information flow of the feature extraction module and the semantic enhancement module,thereby improving the model performance.The core idea is that the medical entity label is obtained by summarizing a large amount of medical data,and the label interpretation is the result of scientific explanation and explanation of the label.The model incorporates these rich prior knowledge in the medical field to make it more accurate.Accurately understand the semantics of entities in the medical domain and improve their recognition.Ex-perimental results show that the method has achieved 0.71％,0.53％and 1.17％improvement on the three baseline models of the Chinese medical entity extraction dataset(CMeEE-V2),and provides an effective method for entity recognition in small sam-ple scenarios.
Chinese medical named entity recognitionLabel knowledgePrior knowledgeAdaptive fusion mechanismFew shot
NETL
NSTL
万方数据
