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A Military Named Entity Recognition Method Combined with Dictionary

Military named entity recognition is the basis of the military intelligence analysis and operational information service. In order to solve the problems of inaccurate word segmentation, various forms of existence and the lack of corpus in military texts, the author proposes a method of military named entity recognition by combining with dictionaries. By constructing authoritative dictionary in the military field, and taking advantage of Bi-directional Long Short-Term Memory (BiLSTM) neural network in dealing with the wide range of contextual information, the Dic+ BiLSTM-CRF named entity recognition model was constructed. The experimental results on the tagged military text corpus show that the extraction effect of this method is further improved than the traditional method.