Text Categorization for Assessing Multiple Documents Integration, or John Henry Visits a Data Mine

  • Peter Hastings
  • Simon Hughes
  • Joe Magliano
  • Susan Goldman
  • Kim Lawless
Conference paper

DOI: 10.1007/978-3-642-21869-9_17

Part of the Lecture Notes in Computer Science book series (LNCS, volume 6738)
Cite this paper as:
Hastings P., Hughes S., Magliano J., Goldman S., Lawless K. (2011) Text Categorization for Assessing Multiple Documents Integration, or John Henry Visits a Data Mine. In: Biswas G., Bull S., Kay J., Mitrovic A. (eds) Artificial Intelligence in Education. AIED 2011. Lecture Notes in Computer Science, vol 6738. Springer, Berlin, Heidelberg

Abstract

A critical need for students in the digital age is to learn how to gather, analyze, evaluate, and synthesize complex and sometimes contradictory information across multiple sources and contexts. Yet reading is most often taught with single sources. In this paper, we explore techniques for analyzing student essays to give feedback to teachers on how well their students deal with multiple texts. We compare the performance of a simple regular expression matcher to Latent Semantic Analysis and to Support Vector Machines, a machine learning approach.

Keywords

Natural Language Processing Machine Learning Corpus Analysis 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Peter Hastings
    • 1
  • Simon Hughes
    • 1
  • Joe Magliano
    • 2
  • Susan Goldman
    • 3
  • Kim Lawless
    • 3
  1. 1.DePaul UniversityUSA
  2. 2.Northern Illinois UniversityUSA
  3. 3.University of IllinoisChicagoUSA

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