Abstract: In order to solve the poor quality problem of domain embedding space caused by inadequate military corpus which makes low accuracy of applying deep neural network model to military named entity recognition,this paper introduces a domain adaptive method to help learn the embedding of military fields from more useful information of additional fields through distributed representation of words.First,we establish the domain dictionary and combine CRF algorithm to perform domain adaptive word segment with the collected general domain and military areas corpus as training corpus for embedding,and word vectors are used as features and spliced with character vectors to enrich the embedding information and to validate the effect of word segmentation.Then the domain adaptive transformation is carried out to the heterogeneous embedded space of the general domain and the military domain,and the domain adaptive embedding is generated,as the input to BiLSTM-CRF layer of base model.At last,the recognition evaluation is carried out through CoNLL-2000.The experimental results show that,under the same model,the recognition precision rate (P),recall rate (R),and integrated F1 value (F1) of the proposed method are improved by 2.17%,1.04%,and 1.59%,respectively,compared with the military field embedding trained by a corpus which is obtained from general word segmentation.

Key words: Character embedding, Chinese word segmentation, Domain adaptation, Named entity recognition, Word embedding