Abstract
The problem of spam short message (SMS) recognition involves many aspects of natural language processing. A good solution to solving the problem can not only improve the quality of people experiencing the mobile life, but also has a positive role on promoting the analysis of short text occurring in current mobile applications, such as Webchat and microblog. As spam SMSes have characteristics of sparsity, transformation and real-timedness, we propose three methods at different levels, i.e., recognition based on symbolic features, recognition based on text similarity, and recognition based on pattern matching. By combining these methods, we obtain a multi-level approach to spam SMS recognition. In order to enrich the pattern base to reduce manual labor and time, we propose a quasi-pattern learning method, which utilizes quasi-pattern matching results in the pattern matching process. The method can learn many interesting and new patterns from the SMS corpus. Finally, a comprehensive analysis indicates that our spam SMS recognition approach achieves a precision rate as high as 95.18%, and a recall rate of 95.51%.
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Weimin Wang received his PhD at the Graduate University of the Chinese Academy of Sciences, China in 2008. He is now working as a lecturer at Jiangsu University of Science and Technology, China. His research interests include data mining, natural language processing, information retrieval and ontology engineering.
Dan Zhou is now pursuing his master degree in University of Chinese Academy of Sciences, China. His research interests include machine learning, natural language processing and ontology engineering.
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Wang, W., Zhou, D. A multi-level approach to highly efficient recognition of Chinese spam short messages. Front. Comput. Sci. 12, 135–145 (2018). https://doi.org/10.1007/s11704-016-5415-8
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DOI: https://doi.org/10.1007/s11704-016-5415-8