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Framework for Text Classification

  • Catarina Silva
  • Bernardete Ribeiro
Part of the Studies in Computational Intelligence book series (SCI, volume 255)

Abstract

The previous chapters presented a number of novel techniques to tackle a variety of problems encountered in real-world text classification settings. The common underlying thread has been the integration of knowledge in the inference of inductive learning models without penalizing processing time. This chapter unifies the main topics of this book into a framework. An inductive inference-based text classification framework will provide basic generic tools that are appropriate for a broad range of applications. New research trends in text classification are highlighted towards the end. We will focus on the particular developments in kernel methods triggered by new problems in text mining and on how to extract useful knowledge by mining relationships between data. We include a few promising research directions that are likely to expand in the future.

Keywords

Text Mining Kernel Method Kernel Principal Component Analysis Latent Semantic Indexing Relevance Vector Machine 
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 2010

Authors and Affiliations

  • Catarina Silva
    • Bernardete Ribeiro

      There are no affiliations available

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