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Exploratory Text Analysis: Data-Driven versus Human Semantic Similarity Judgments

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Adaptive and Natural Computing Algorithms (ICANNGA 2013)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7824))

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Abstract

We present an approach for comparing human-made and automatically generated semantic representations with an assumption that neither of these has a primary status over the other. In the experimental part, we compare the results gained by using independent component analysis and the self-organizing map algorithm on word context analysis with a semantically labeled dictionary called BLESS. The data-driven methods are useful in assessing the quality of the hand-created semantic resources and these resources can be used to evaluate the outcome of the automated process. We present a number of specific findings that go beyond typical quantitative evaluations of the results of data-driven methods in which the manually created resources are usually taken as a gold standard.

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Author information

Authors and Affiliations

  1. Department of Information and Computer Science, Aalto University School of Science, P.O. Box 15400, FI-00076, Aalto, Finland

    Tiina Lindh-Knuutila & Timo Honkela

Authors
  1. Tiina Lindh-Knuutila
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  2. Timo Honkela
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Editors and Affiliations

  1. Départment des Systémes d’Information, Quartier UNIL-Dorigny, Bâtiment Internef, Université de Lausanne, 105, Lausanne, Switzerland

    Marco Tomassini , Alberto Antonioni , Fabio Daolio  & Pierre Buesser , ,  & 

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Lindh-Knuutila, T., Honkela, T. (2013). Exploratory Text Analysis: Data-Driven versus Human Semantic Similarity Judgments. In: Tomassini, M., Antonioni, A., Daolio, F., Buesser, P. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2013. Lecture Notes in Computer Science, vol 7824. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37213-1_44

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  • DOI: https://doi.org/10.1007/978-3-642-37213-1_44

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37212-4

  • Online ISBN: 978-3-642-37213-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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