Abstract: Traditional named entity recognition methods,which tag words by inputting a good deal of handmade features into statistics learning models,have achieved good results,but the manual mode of defining features makes it more difficult to build the model.To decrease the workload of the manual mode,this paper firstly got the distributed representation of word features by training the neural network language model without supervision,then discovered the deep features of words by inputting the distributed features into the deep belief net,finally conducted named entity recognition.The method uses the deep belief net to extend the neural network language model on the basis of research of predecessors,and presents a deep architecture which is available for named entity recognition.Experiments show that the me-thod applied to named entity recognition can perform better than traditional conditional random field model if both only using term feature and POS feature,and has a certain use value.

Key words: Deep belief net,Named entity recognition,Neural network language model