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Representing and Analysing Meaning with LSA

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Learning Analytics in R with SNA, LSA, and MPIA

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Abstract

Latent Semantic Analysis (LSA) is a time-tested algorithm for representing and analysing meaning from text, with its closeness in mathematical foundation being a natural candidate for further integration. The chapter starts with its mathematical foundations, then provides an overview on the standard analysis workflow (with the package developed), also guiding through the standard use cases. Two demos follow subsequently to foster understanding and gain insight into the main restrictions applying to LSA. The foundational example presented picks up the usage scenario of the foundational SNA demo of the previous chapter. Following a summary of the state of the art in application of LSA to technology-enhanced learning, a second, real-life application example in essay scoring will be added. A summary outlining also the key limitations of LSA concludes the chapter.

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Notes

  1. 1.

    14 instead of 9 documents.

  2. 2.

    Landauer and Foltz first met at Bellcore: http://kt.pearsonassessments.com/whyChooseUs.php

  3. 3.

    http://furnas.people.si.umich.edu/BioVita/publist.htm

  4. 4.

    http://en.wikipedia.org/wiki/Scott_Deerwester

  5. 5.

    http://psychology.uwo.ca/faculty/harshman/

  6. 6.

    The poverty of stimulus problem is paraphrased as: “How do people know as much as they do with as little information as they get?” (Landauer and Dumais 1997, p. 211)

  7. 7.

    The author made this example available online at: http://crunch.kmi.open.ac.uk/people/~fwild/services/lsa-essay-scoring.Rmw

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    Fridolin Wild

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Wild, F. (2016). Representing and Analysing Meaning with LSA. In: Learning Analytics in R with SNA, LSA, and MPIA. Springer, Cham. https://doi.org/10.1007/978-3-319-28791-1_4

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  • DOI: https://doi.org/10.1007/978-3-319-28791-1_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28789-8

  • Online ISBN: 978-3-319-28791-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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