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Knowledge and Information Systems

, Volume 61, Issue 3, pp 1485–1516 | Cite as

Combining semantic and term frequency similarities for text clustering

  • Victor Hugo Andrade SoaresEmail author
  • Ricardo J. G. B. Campello
  • Seyednaser Nourashrafeddin
  • Evangelos Milios
  • Murilo Coelho Naldi
Regular Paper
  • 349 Downloads

Abstract

A key challenge for document clustering consists in finding a proper similarity measure for text documents that enables the generation of cohesive groups. Measures based on the classic bag-of-words model take into account solely the presence (and frequency) of words in documents. In doing so, semantically similar documents which use different vocabularies may end up in different clusters. For this reason, semantic similarity measures that use external knowledge, such as word n-gram corpora or thesauri, have been proposed in the literature. In this paper, the Frequency Google Tri-gram Measure is proposed to assess similarity between documents based on the frequencies of terms in the compared documents as well as the Google n-gram corpus as an additional semantic similarity source. Clustering algorithms are applied to several real datasets in order to experimentally evaluate the quality of the clusters obtained with the proposed measure and compare it with a number of state-of-the-art measures from the literature. The experimental results demonstrate that the proposed measure improves significantly the quality of document clustering, based on statistical tests. We further demonstrate that clustering results combining bag-of-words and semantic similarity are superior to those obtained with either approach independently.

Keywords

Document clustering Similarity measure Semantic similarity Text mining 

Notes

Acknowledgements

The authors acknowledge the Brazilian Research Agencies Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)—Finance Code 001, CNPq, FAPEMIG and FAPESP, the Natural Sciences and Engineering Research Council of Canada, the Boeing Company, CALDO, and the International Development Research Centre, Ottawa, Canada, for their financial support to this work.

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  • Victor Hugo Andrade Soares
    • 1
    • 3
    Email author
  • Ricardo J. G. B. Campello
    • 2
    • 3
  • Seyednaser Nourashrafeddin
    • 4
  • Evangelos Milios
    • 4
  • Murilo Coelho Naldi
    • 1
    • 5
  1. 1.Department of InformaticsFederal University of Viçosa (UFV)ViçosaBrazil
  2. 2.School of Mathematical and Physical SciencesThe University of NewcastleCallaghanAustralia
  3. 3.Institute of Mathematics and Computer SciencesUniversity of São Paulo (USP)São CarlosBrazil
  4. 4.Faculty of Computer ScienceDalhousie UniversityHalifaxCanada
  5. 5.Department of Computer ScienceFederal University of São Carlos (UFSCar)São CarlosBrazil

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