Language Resources and Evaluation

, Volume 47, Issue 1, pp 151–178 | Cite as

Classifying unlabeled short texts using a fuzzy declarative approach

  • Francisco P. RomeroEmail author
  • Pascual Julián-Iranzo
  • Andrés Soto
  • Mateus Ferreira-Satler
  • Juan Gallardo-Casero
Original Paper


Web 2.0 provides user-friendly tools that allow persons to create and publish content online. User generated content often takes the form of short texts (e.g., blog posts, news feeds, snippets, etc). This has motivated an increasing interest on the analysis of short texts and, specifically, on their categorisation. Text categorisation is the task of classifying documents into a certain number of predefined categories. Traditional text classification techniques are mainly based on word frequency statistical analysis and have been proved inadequate for the classification of short texts where word occurrence is too small. On the other hand, the classic approach to text categorization is based on a learning process that requires a large number of labeled training texts to achieve an accurate performance. However labeled documents might not be available, when unlabeled documents can be easily collected. This paper presents an approach to text categorisation which does not need a pre-classified set of training documents. The proposed method only requires the category names as user input. Each one of these categories is defined by means of an ontology of terms modelled by a set of what we call proximity equations. Hence, our method is not category occurrence frequency based, but highly depends on the definition of that category and how the text fits that definition. Therefore, the proposed approach is an appropriate method for short text classification where the frequency of occurrence of a category is very small or even zero. Another feature of our method is that the classification process is based on the ability of an extension of the standard Prolog language, named Bousi~Prolog , for flexible matching and knowledge representation. This declarative approach provides a text classifier which is quick and easy to build, and a classification process which is easy for the user to understand. The results of experiments showed that the proposed method achieved a reasonably useful performance.


Text categorization Ontologies Thesauri Unlabeled short texts 



This research was partially supported by the Spanish Ministry of Science and Innovation (MEC) under TIN2007-67494 and TIN2010-20395 projects and by the Regional Government of Castilla-La Mancha under PEIC09-0196-3018 and PII1I09-0117-4481 projects


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Copyright information

© Springer Science+Business Media Dordrecht 2012

Authors and Affiliations

  • Francisco P. Romero
    • 1
    Email author
  • Pascual Julián-Iranzo
    • 1
  • Andrés Soto
    • 2
  • Mateus Ferreira-Satler
    • 1
  • Juan Gallardo-Casero
    • 1
  1. 1.Department of Information Technologies and SystemsUniversity of Castilla La ManchaCiudad RealSpain
  2. 2.Department of Computer ScienceUniversidad Autònoma del CarmenCampecheMèxico

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