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Grouping Co-occurrence Filtering Based on Bayesian Filtering

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Book cover Advanced Research in Applied Artificial Intelligence (IEA/AIE 2012)

Abstract

Recently, many people are using communication tools on the Web, but some send harmful information to others. Most operators manually deal with harmful information, which is expensive. In this paper, we implement two-word co-occurrence filtering by applying the Bayesian filtering method as a spam filter. We propose grouping co-occurrence filtering based on Bayesian filtering and experimentally verify our approach. Grouping co-occurence filtering detect harmful or safe documents at low cost. Our result suggests that grouping co-occurrence filtering is more stable and has a higher accuracy than co-occurrence filtering baesd on Bayesian filtering.

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References

  1. Cellular phone companies were requested to promote harmful site access restriction services (filtering service) on mobile phones and PHSs, http://www.soumu.go.jp/menu_news/s-news/2007/071210_4.html

  2. Graham, P.: A plan for spam. In: Graham, P. (ed.) Hackers and Painters. OReilly (2004)

    Google Scholar 

  3. Graham, P.: Better bayesian filtering. In: Proceedings of the 2003 Spam Conference (2003)

    Google Scholar 

  4. Gray, R.: A statistical approach to the spam problem, http://www.linuxjournal.com/article/6467/

  5. Gray, R.: Spam detection (2002), http://radio.weblogs.com/0101454/stories/2002/09/16/spamDetection.html

  6. Mera, K., Ichimura, T., Aizawa, T., Yamashita, T.: Invoking emotions in a dialog system based on word-impressions. Trans. Japanese Society for Artificial Intelligence 173, 186–195 (2002)

    Article  Google Scholar 

  7. Nagata, M., Taira, H.: Text classification, Trade fair of learning theories. IPSJ Magazine 42(1), 32–37 (2001)

    Google Scholar 

  8. Tsuda, Y.: Text Categorization Using Native Bayes Model Based on Co-Occurrence words. In: Symposium on Information Theory and its Applications, SITA (2006)

    Google Scholar 

  9. Manning, C.D., Schutze, H.: Foundations of statistical natural language perspectives. Oxford Univ. Press, New York (1999); Guage processing. MIT Press, Cambridge (1999)

    Google Scholar 

  10. Ando, S., Fujii, Y., Ito, T.: Filtering Harmful Sentences based on Multiple Word Co-occurrence. In: 2010 IEEE/ACIS 9th International Conference on Computer and Information Science (2010)

    Google Scholar 

  11. Matsuo, Y., Ishizuka, M.: Keyword Extraction from a Document using Word Co-occurrence Statistical Information. Transactions of the Japanese Society for Artificial Intelligence 17(3), 217–223

    Google Scholar 

  12. Cortes, C., Vapnik, V.: Support Vector Networks. Machine Learning 20, 273–297 (1995)

    MATH  Google Scholar 

  13. Kobayashi, D., Ishizuka, M.: Classification of Spam Posts on Knowledge Searching Website. In: The 21st Annual Conference of the JSAI (2007)

    Google Scholar 

  14. Kumamoto, T., Tanaka, K.: Proposal of Impression Mining from News Articles. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds.) KES 2005, Part I. LNCS (LNAI), vol. 3681, pp. 901–910. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  15. Turney, P.D.: Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In: Proc. Conference on ACL (2002)

    Google Scholar 

  16. Mecab, http://mecab.sourceforge.net/

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Yoshimura, T., Fujii, Y., Ito, T. (2012). Grouping Co-occurrence Filtering Based on Bayesian Filtering. In: Jiang, H., Ding, W., Ali, M., Wu, X. (eds) Advanced Research in Applied Artificial Intelligence. IEA/AIE 2012. Lecture Notes in Computer Science(), vol 7345. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31087-4_4

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  • DOI: https://doi.org/10.1007/978-3-642-31087-4_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31086-7

  • Online ISBN: 978-3-642-31087-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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