Compact Representation of Documents Using Terms and Termsets

  • Dima Badawi
  • Hakan AltınçayEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10934)


In this study, computation of compact document vectors by utilizing both terms and termsets for binary text categorization is addressed. In general, termsets are concatenated with all terms, leading to large document vectors. Selection of a subset of terms and termsets for compact but also effective representation of documents is considered in this study. Two different methods are studied for this purpose. In the first method, combination of terms and termsets in different proportions is evaluated. As an alternative approach, normalized ranking scores of terms and termsets are employed for subset selection. Experiments conducted on two widely used datasets have shown that termsets can effectively complement terms also in cases when small number of features are used to represent documents.


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Computer DepartmentPalestine Technical University - KadoorieHebronPalestine
  2. 2.Computer Engineering DepartmentEastern Mediterranean UniversityFamagusta, North CyprusTurkey

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