Abstract: Aiming at the problem that the traditional named entity recognition method relies heavily on plenty of hand-crafted features,domain knowledge,word segmentation effect,and does not make full use of word order information,anamed entity recognition model based on BGRU(bidirectional gated recurrent unit) was proposed.This model utilizes external data and integrates potential word information into character-based BGRU-CRF by pre-training words into dictionaries on large automatic word segmentation texts,making full use of the information of potentialwords,extracting comprehensive information of context,and more effectively avoiding ambiguity of entity.In addition,attention mechanism is used to allocate the weight of specific information in BGRU network structure,which can select the most relevant characters and words from the sentence,effectively obtain long-distance dependence of specific words in the text,recognize the classification of information expression,and identify named entities.The model explicitly uses the sequence information between words,and is not affected by word segmentation errors.Compared with the traditional sequence labeling model and the neural network model,the experimental results on MSRA and OntoNotes show that the proposed model is 3.08% and 0.16% higher than the state-of-art complaint models on the overall F1 value respectively.

Key words: Attention mechanism, Bidirectional gated recurrent unit, Named entity recognition