Toward principles for the design of ontologies used for knowledge sharing
International Journal of Human-Computer Studies - Special issue: the role of formal ontology in the information technology
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
A declarative semantics for Bousi~Prolog
PPDP '09 Proceedings of the 11th ACM SIGPLAN conference on Principles and practice of declarative programming
WordNet::Similarity: measuring the relatedness of concepts
HLT-NAACL--Demonstrations '04 Demonstration Papers at HLT-NAACL 2004
Text categorization from category name via lexical reference
NAACL-Short '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers
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The classic approach to text categorisation is based on a learning process that requires a large number of labelled training texts to achieve an accurate performance. The most notable problem is that labelled texts are difficult to generate because categorising shorts texts as snippets or messages must be done by human developers, although unlabelled short texts could be easily collected. In this paper, we present an approach to categorising unlabelled short texts which only require, as user input, the category names defined by means of an ontology of terms modelled by a set of proximity equations. The proposed classification process is based on the ability of a fuzzy extension of the standard Prolog language named Bousi~Prolog for flexible matching and knowledge representation. This declarative approach provides a text classifier which is fast and easy to build, as well as a classification process that is easy for the user to understand. The results of the experiment showed that the proposed method achieved a reasonably good performance.