Automatic Tagging of Texts with Contextual Factors Using Knowledge Concepts

  • Rajendra Prasath
  • Philip O’Reilly
  • Aidan Duane
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8284)

Abstract

We present a method to perform automatic tagging of contextual factors associated with mobile payments data. Users specify a short description about the contextual factors interesting to them. The proposed system characterizes these factors and generates the knowledge concepts, similar to [1,2], but with the help of corpus statistics. These knowledge concepts describe the factors in terms of multi-faceted information search. Secondly, given a query, the underlying retrieval system retrieves top k texts pertaining to user information needs. Then based on the similarity between each of the knowledge concepts and the best matching texts, the context matching score is computed. Then the ranked sequence of contextual tags are assigned to the each retrieved text. The experimental results show that the proposed approach characterizes the context from user specified factors and performs the contextual tagging of the retrieved texts in a better way.

Keywords

Knowledge Concepts Contextual Tagging Mobile Payments Learning from Data 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Gabrilovich, E., Markovitch, S.: Computing semantic relatedness using wikipedia-based explicit semantic analysis. In: Proc. of the 20th Int. Joint conf. on Artifical intelligence, IJCAI 2007, pp. 1606–1611. Morgan Kaufmann Publishers Inc. (2007)Google Scholar
  2. 2.
    Prasath, R., Sarkar, S.: Unsupervised feature generation using knowledge repositories for effective text categorization. In: Proceedings of the 2010 Conference on ECAI 2010: 19th European Conference on Artificial Intelligence, pp. 1101–1102. IOS Press, Amsterdam (2010)Google Scholar
  3. 3.
    Pedersen, T., Kulkarni, A.: Identifying similar words and contexts in natural language with senseclusters. In: Proc. of the 20th National Conf. on Artificial Intelligence, AAAI 2005, pp. 1694–1695. AAAI Press (2005)Google Scholar
  4. 4.
    Johnson, W., Lindenstrauss, L.: Extensions of lipschitz maps into a hilbert space. Contemporary Mathematics 26, 189–206 (1984)CrossRefMATHMathSciNetGoogle Scholar
  5. 5.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)MATHGoogle Scholar
  6. 6.
    Blei, D.M.: Probabilistic topic models. Commun. ACM 55(4), 77–84 (2012)CrossRefMathSciNetGoogle Scholar
  7. 7.
    Griffiths, T.: Gibbs sampling in the generative model of latent dirichlet allocation. Technical report, Stanford University (2002)Google Scholar
  8. 8.
    Brown, L.G.: Convenience in services marketing. Journal of Services Marketing 4(1), 53–59 (1990)CrossRefGoogle Scholar
  9. 9.
    Duane, A., O’Reilly, P., Andreev, P.: Trusting m-payments - realising the potential of smart phones for m-commerce: A conceptual model & survey of consumers in ireland. In: ICIS 2011 (2011)Google Scholar
  10. 10.
    Kelman, H.C.: Compliance, identification, and internalization three processes of attitude change. Journal of Conflict Resolution 2(1), 51–60 (1958)CrossRefMathSciNetGoogle Scholar
  11. 11.
    O’Reilly, P., Duane, A., Andreev, P.: To m-pay or not to m-pay - realising the potential of smart phones: conceptual modeling and empirical validation. Electronic Markets 22(4), 229–241 (2012)CrossRefGoogle Scholar
  12. 12.
    Schierz, P.G., Schilke, O., Wirtz, B.W.: Understanding consumer acceptance of mobile payment services: An empirical analysis. Electron. Commer. Rec. Appl. 9(3), 209–216 (2010)CrossRefGoogle Scholar
  13. 13.
    Roca, J.C., García, J.J., de la Vega, J.J.: The importance of perceived trust, security and privacy in online trading systems. Inf. Manag. Comput. Security 17(2), 96–113 (2009)Google Scholar
  14. 14.
    Mallat, N.: Exploring consumer adoption of mobile payments - a qualitative study. J. Strateg. Inf. Syst. 16(4), 413–432 (2007)CrossRefGoogle Scholar
  15. 15.
    Lee, S.Y.: Examining the factors that influence early adopters’ smartphone adoption: The case of college students. Telematics and Informatics (to appear, 2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Rajendra Prasath
    • 1
  • Philip O’Reilly
    • 1
  • Aidan Duane
    • 2
  1. 1.University College Cork (UCC)CorkIreland
  2. 2.Waterford Institute of TechnologyWaterfordIreland

Personalised recommendations