Abstract: The identification of military named entities can provide automatic auxiliary support for intelligence analysis, command and decision-making, and is the key technical means to improve the intelligence of command information system. Because of the differences in Chinese and English language characteristics, Chinese entity recognition must first part the text, and word-breaks will lead to the accumulation of errors in the recognition of named entities. In addition, the identification of named entities in a piece of text may be related only to local information, and each word contributes differently to other entities, and too much redundant information can only negatively affect the identification of named entities. In response to the above problems, we propose a network model of Lattice-Long Memory Neural Network (LSTM) combined with self-attention mechanisms. The Lattice LSTM structure enables the identification of proper nouns in sentences and integrates potential word information into character-based LSTM-CRF models. Self-attention structures can capture syntactic or semantic features between words in the same sentence. Model experiments were conducted on a small sample set that we labeled ourselves, and the results show that our model achieves the desired effect.

Key words: named entity recognition, Lattice, group；self-attention