Abstract
As the carrier of information transmission, the internet inevitably contains much bad information. In view of this phenomenon, with the purpose of identifying the bad information in the network, we combine existing Chinese text mining technology for experimental research. In combination with the idea of AlphaGo double decision system, the experiment will deal with the text information identification and classification using two system models, so that more accurate results can be obtained. In the experiment, a system does text segmentation and feature selection. Another system uses this method based on rules and statistics to compare text to determine whether or not it is bad information based on the established bad information database. And finally the two system carry out the text classification work. In the meantime, the two-system model worked together to identify and classify the bad information. AlphaGo’s strategy was used to combine the former decentralized methods to make the system as a whole. This enables the system to improve the execution efficiency without reducing the recall rate, and the identification and classification accuracy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Su, G.Y., Ma, Y.H., Li, J.H.: One improved content based information filtering model. J. Shanghai Jiao Tong Univ. 12, 2030–2034 (2014)
Xiong, J.X., Li, S.H.: Research on the concept network technique to the bad text information. Comput. Eng. Appl. 03, 183–186 (2016)
Fan, X.H., Wang, P., Zhou, P.: Two-step text orientation identification based on feature extension. Comput. Eng. Appl. 48(01), 162–165 (2012)
Dong, F.: How does AlphaGo play chess[EB/OL]. http://tech.qq.com/a/20160311/050990.html. Accessed 21 Mar 2018
Qin, Z.: Research and implementation of Chinese word segmentation algorithm. Jilin University (2016)
Roy, A., Phadikar, S.: Automatic segmentation of spoken word signals into letters based on amplitude variation for speech to text transcription. In: Mandal, J.K., Satapathy, S.C., Sanyal, M.K., Sarkar, P.P., Mukhopadhyay, A. (eds.) Information Systems Design and Intelligent Applications. AISC, vol. 340, pp. 621–628. Springer, New Delhi (2015). https://doi.org/10.1007/978-81-322-2247-7_63
Yang, Z., Wang, J., Evans, D., et al.: AutoReplica: automatic data replica manager in distributed caching and data processing systems. In: Performance Computing and Communications Conference. IEEE (2017)
Chu, X.L.: Machine Learning Based Patent Categorization. Shanghai Jiao Tong University (2008)
Veber, J.: Text classification: classifying plain source files with neural network. J. Syst. Integr. 1(4), 39–44 (2010)
National Language Commission Language Resources Network. http://www.clr.org.cn/clr.jsp. Accessed 11 Apr 2018
Liu, T., Che, W.X., Li, Z.H.: Language technology platform. J. Chin. Inf. Process. 25(06), 53–62 (2011)
Acknowledgments
This work was supported by the key projects China Language committee Research Project NO. ZDI135-13.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Chen, H., Zheng, Z., Chen, Q., Yang, L., Chen, X., Zheng, S. (2018). The Application Research of AlphaGo Double Decision System in Network Bad Information Recognition. In: Huang, DS., Gromiha, M., Han, K., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2018. Lecture Notes in Computer Science(), vol 10956. Springer, Cham. https://doi.org/10.1007/978-3-319-95957-3_32
Download citation
DOI: https://doi.org/10.1007/978-3-319-95957-3_32
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-95956-6
Online ISBN: 978-3-319-95957-3
eBook Packages: Computer ScienceComputer Science (R0)