Nested named entity recognition model based on dual stream feature complementation was proposed to solve the prob-lem that the previous sentences can not obtain efficient feature information after text coding.Sentences were embedded in two ways of word level and character level.The sentence context information was obtained through the neural network BiLSTM.The two vectors entered the feature complementation module at the low level and the high level.The entity word recognition module and the fine-grained partition module finely partitioned the entity word interval to obtain the internal entities.Experimen-tal results show that the model has great improvements in feature extraction compared with the classical model.
关键词
命名实体识别/自然语言处理/嵌套结构/双流特征互补/神经网络/实体词识别/细粒度划分
Key words
named entity recognition/NLP/nested structure/dual-stream feature complementation/neural network/entity word recognition/fine grain division
维普
万方数据