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How Document Properties Affect Document Relatedness Measures

  • Conference paper
Computational Linguistics and Intelligent Text Processing (CICLing 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8404))

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Abstract

We address the question of how document properties (word count, term frequency, cohesiveness, genre) affect the quality of unsupervised document relatedness measures (Google trigram model and vector space model). We use three genres of documents: aviation safety reports, medical equipment failure descriptions, and biodiversity heritage library text. Quality of document relatedness is assessed by the accuracy of a classification task using the kNN method. Experiments discover correlations between document property values and document relatedness quality, and we discuss how one approach may perform better depending on property values of the dataset.

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Author information

Authors and Affiliations

  1. Faculty of Computer Science, Dalhousie University, Halifax, NS, Canada, B3H 4R2

    Jessica Perrie, Aminul Islam & Evangelos Milios

Authors
  1. Jessica Perrie
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  2. Aminul Islam
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  3. Evangelos Milios
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Editors and Affiliations

  1. Center for Computing Research, National Polytechnic Institute, Av. Juan Dios Bátiz, Col. Nueva Industrial Vallejo, 07738, Mexico D.F, Mexico

    Alexander Gelbukh

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Perrie, J., Islam, A., Milios, E. (2014). How Document Properties Affect Document Relatedness Measures. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2014. Lecture Notes in Computer Science, vol 8404. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54903-8_33

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  • DOI: https://doi.org/10.1007/978-3-642-54903-8_33

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-54902-1

  • Online ISBN: 978-3-642-54903-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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