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Machine Learning for Text: An Introduction

  • Charu C. Aggarwal
Chapter

Abstract

The extraction of useful insights from text with various types of statistical algorithms is referred to as text mining, text analytics, or machine learning from text. The choice of terminology largely depends on the base community of the practitioner. This book will use these terms interchangeably. Text analytics has become increasingly popular in recent years because of the ubiquity of text data on the Web, social networks, emails, digital libraries, and chat sites.

Bibliography

  1. [2]
    C. Aggarwal. Data mining: The textbook. Springer, 2015.Google Scholar
  2. [14]
    C. Aggarwal, and C. Zhai, Mining text data. Springer, 2012.Google Scholar
  3. [31]
    R. Baeza-Yates, and B. Ribeiro-Neto. Modern information retrieval. ACM press, 2011.Google Scholar
  4. [36]
    R. Banchs. Text Mining with MATLAB. Springer, 2012.Google Scholar
  5. [50]
    C. M. Bishop. Pattern recognition and machine learning. Springer, 2007.Google Scholar
  6. [71]
    S. Buttcher, C. Clarke, and G. V. Cormack. Information retrieval: Implementing and evaluating search engines. The MIT Press, 2010.Google Scholar
  7. [79]
    S. Chakrabarti. Mining the Web: Discovering knowledge from hypertext data. Morgan Kaufmann, 2003.Google Scholar
  8. [120]
    W. B. Croft, D. Metzler, and T. Strohman. Search engines: Information retrieval in practice, Addison-Wesley Publishing Company, 2009.Google Scholar
  9. [168]
    R. Feldman and J. Sanger. The text mining handbook: advanced approaches in analyzing unstructured data. Cambridge University Press, 2007.Google Scholar
  10. [204]
    J. Han, M. Kamber, and J. Pei. Data mining: concepts and techniques. Morgan Kaufmann, 2011.Google Scholar
  11. [206]
    T. Hastie, R. Tibshirani, and J. Friedman. The elements of statistical learning. Springer, 2009.Google Scholar
  12. [249]
    D. Jurafsky and J. Martin. Speech and language processing. Prentice Hall, 2008.Google Scholar
  13. [303]
    B. Liu. Web data mining: exploring hyperlinks, contents, and usage data. Springer, New York, 2007.Google Scholar
  14. [321]
    C. Manning, P. Raghavan, and H. Schütze. Introduction to information retrieval. Cambridge University Press, Cambridge, 2008.Google Scholar
  15. [322]
    C. Manning and H. Schütze. Foundations of statistical natural language processing. MIT Press, 1999.Google Scholar
  16. [325]
    A. McCallum. Bow: A toolkit for statistical language modeling, text retrieval, classification and clustering. http://www.cs.cmu.edu/~mccallum/bow, 1996.
  17. [349]
    T. M. Mitchell. Machine learning. McGraw Hill International Edition, 1997.Google Scholar
  18. [355]
    F. Moosmann, B. Triggs, and F. Jurie. Fast Discriminative visual codebooks using randomized clustering forests. NIPS Conference, pp. 985–992, 2006.Google Scholar
  19. [424]
    G. Salton and M. J. McGill. Introduction to modern information retrieval. McGraw Hill, 1986.Google Scholar
  20. [469]
    P.-N Tan, M. Steinbach, and V. Kumar. Introduction to data mining. Addison-Wesley, 2005.Google Scholar
  21. [491]
    S. Weiss, N. Indurkhya, and T. Zhang. Fundamentals of predictive text mining. Springer, 2015.Google Scholar
  22. [529]
    C. Zhai and S. Massung. Text data management and mining: A practical introduction to information retrieval and text mining. Association of Computing Machinery/Morgan and Claypool Publishers, 2016.Google Scholar
  23. [549]
  24. [550]
  25. [551]
  26. [553]
  27. [554]
  28. [556]

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Charu C. Aggarwal
    • 1
  1. 1.IBM T. J. Watson Research CenterYorktown HeightsUSA

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