Aiming at classifying the police text descriptions of city’s public security for police stations, this paper builds a text classification of police descriptions based on the existing text classification methods used in other industries. By demonstrating the applicable occasions of common classification networks and their advantages and disadvantages, and combining with the text characteristics of the police case description data, a network structure based on Improved convolutional reccurrent neural network (CRNN) is proposed. The proposed structure provides an optimization key feature extraction process to make up the insufficiency of the existing model in the extraction of short-text feature. Through the comparison test between the proposed model and the existing common classification model, the proposed model not only shows an improved classification accuracy, 2%～3% higher than the existing model, but also provides effective guarantee on the relevance of local features of the data. The model can achieve accurate type classification of police descriptions, thus improving the automation efficiency of the police station.
WANG Mengxuan, ZHANG Sheng, WANG Yue, LEI Ting, DU Wen
. Research and Application of Improved CRNN Model in Classification of Alarm Texts[J]. Journal of Applied Sciences, 2020
, 38(3)
: 388
-400
.
DOI: 10.3969/j.issn.0255-8297.2020.03.005
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