Abstract
The information age poses new challenges to the automatic and rapid classification of public security data. To get rid of the low-efficiency work mode of manual identification, perform rapid automatic classification of police situation, and improve the performance and accuracy of classifiers, many models and algorithms have been proposed. The traditional police situation classification methods almost always use traditional machine learning algorithms. The shortcomings of complex working mode, heavy workload, and poor learning effect have led to the rise of deep learning classification methods. This paper proposes the ATextCNN model, which introduces an attention mechanism in the Input Layer based on the TextCNN model, and uses a word filtering algorithm to calculate the contribution of words to filter nonsense words. The ATextCNN model develops a multi-classification model based on deep learning. We conduct experiments on two real data sets and divided data into nine categories. Experiments show that the ATextCNN model is better than the traditional classification methods in terms of accuracy and efficiency when performing multi-classification tasks, and is better than the state of the art method in practical application. Therefore, our method is more conducive to police situation identification and management.
This work is supported in part by National Natural Science Foundation of China (61728204), Innovation Funding (NJ20160028, NT2018027, NT2018028, NS2018057), Aeronautical Science Foundation of China (2016551500), State Key Laboratory for smart grid protection and operation control Foundation, Association of Chinese Graduate Education (ACGE).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Choi, E., Bahadori, M.T., Sun, J., Kulas, J., Schuetz, A., Stewart, W.: Retain: an interpretable predictive model for healthcare using reverse time attention mechanism. In: Advances in Neural Information Processing Systems, pp. 3504–3512 (2016)
He, R., Lee, W.S., Ng, H.T., Dahlmeier, D.: An unsupervised neural attention model for aspect extraction. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 388–397 (2017)
Hinton, G.E., Osindero, S., Teh, Y.W.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006)
Hu, C., Xiaowei, Z.: Sentiment analysis based on word vector technology and hybrid neural network. Appl. Res. Comput. 35(12), 3556–3559+3574 (2018)
Jagannatha, A.N., Yu, H.: Structured prediction models for RNN based sequence labeling in clinical text. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing, vol. 2016, p. 856. NIH Public Access (2016)
Jin, G., Zhu, S., Lin, X.: Analysis and prediction on crime in china (2017–2018). J. People’s Public Secur. Univ. Chin. (Soc. Sci. Ed.) 34, 29–38 (2018)
Jin, Z., Han, Y., Zhu, Q.: A sentiment analysis model with the combination of deep learning and ensemble learning. J Harbin Inst Technol 50(11), 32–39 (2018)
Joachims, T.: Text categorization with support vector machines: learning with many relevant features. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, pp. 137–142. Springer, Heidelberg (1998). https://doi.org/10.1007/BFb0026683
Khamar, K.: Short text classification using KNN based on distance function. Int. J. Adv. Res. Comput. Commun. Eng. 2(4), 1916–1919 (2013)
Kim, Y.: Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882 (2014)
Liu, Q., Liang, B., Xu, J.: A deep hierarchical neural network model for aspect-based sentiment analysis. Chin. J. Comput. 41(12), 2637–2652 (2018)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)
Mnih, V., Heess, N., Graves, A., et al.: Recurrent models of visual attention. In: Advances in Neural Information Processing Systems, pp. 2204–2212 (2014)
Prasetijo, A.B., Isnanto, R.R., Eridani, D., Soetrisno, Y.A.A., Arfan, M., Sofwan, A.: Hoax detection system on indonesian news sites based on text classification using SVM and SGD. In: 2017 4th International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE), pp. 45–49. IEEE (2017)
Wang, S., Huang, M., Deng, Z.: Densely connected CNN with multi-scale feature attention for text classification. In: IJCAI, pp. 4468–4474 (2018)
Wang, W., Li, B., Feng, D., Zhang, A., Wan, S.: The OL-DAWE model: tweet polarity sentiment analysis with data augmentation. IEEE Access 8, 40118–40128 (2020)
Zhu, Y., Shang, M., Liang, H.: Design and implementation of automatic categorization system of public security information. Master’s thesis, University of Electronic Science and Technology of China (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Wang, W., Feng, D., Li, B., Tian, J. (2020). ATextCNN Model: A New Multi-classification Method for Police Situation. In: Yang, X., Wang, CD., Islam, M.S., Zhang, Z. (eds) Advanced Data Mining and Applications. ADMA 2020. Lecture Notes in Computer Science(), vol 12447. Springer, Cham. https://doi.org/10.1007/978-3-030-65390-3_11
Download citation
DOI: https://doi.org/10.1007/978-3-030-65390-3_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-65389-7
Online ISBN: 978-3-030-65390-3
eBook Packages: Computer ScienceComputer Science (R0)