**Dong Xu, Ruping Ge \* and Zhihua Niu**

School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China; dxu@shu.edu.cn (D.X.); ZhNiu@shu.edu.cn (Z.N.)

**\*** Correspondence: geruping@shu.edu.cn; Tel.: +86-188-1760-8630

Received: 31 October 2018; Accepted: 8 January 2019; Published: 14 January 2019

**Abstract:** A state-of-the-art entity recognition system relies on deep learning under data-driven conditions. In this paper, we combine deep learning with linguistic features and propose the long short-term memory-conditional random field model (LSTM-CRF model) with the integrity algorithm. This approach is primarily based on the use of part-of-speech (POS) syntactic rules to correct the boundaries of LSTM-CRF model annotations and improve its performance by raising the integrity of the elements. The method incorporates the advantages of the data-driven method and dependency syntax, and improves the precision rate of the elements without losing recall rate. Experiments show that the integrity algorithm is not only easy to combine with the other neural network model, but the overall effect is better than several advanced methods. In addition, we conducted cross-domain experiments based on a multi-industry corpus in the financial field. The results indicate that the method can be applied to other industries.

**Keywords:** LSTM-CRF model; elements recognition; linguistic features; POS syntactic rules
