Semantic Domains in Text Categorization

  • Alfio Gliozzo
  • Carlo Strapparava


In the previous chapter we have shown that DMs provide a very exible and cheap solution for the problem of modeling domain knowledge. In particular, DMs have been used to define a Domain Space, in which the similarity among terms and texts is estimated.

We will show how to exploit DMs inside a supervised machine learning framework, in order to provide “external” knowledge to supervised NLP systems, which can be profitably used for topic similarity estimation. In particular we exploit a Domain Kernel (defined in Sect. 3.7), a similarity function among terms and texts that can be used by any kernel-based learning algorithm, with the effect of avoiding the problems of lexical variability and ambiguity.

In this chapter we show the advantages of domain-based feature representation in supervised learning, approaching the problem of Text Categorization. In particular we will evaluate the Domain Kernel in two tasks: Text Categorization (see Sect. 4.1) and Intensional Learning (see Sect. 4.2).


Label Data Unlabeled Data Domain Space Semantic Domain Intensional Learn 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  1. 1.FBK-irstPovo-TrentoItaly

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