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Text Classification with Active Learning

  • Blaž Novak
  • Dunja Mladenič
  • Marko Grobelnik
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

In many real world machine learning tasks, labeled training examples are expensive to obtain, while at the same time there is a lot of unlabeled examples available. One such class of learning problems is text classification. Active learning strives to reduce the required labeling effort while retaining the accuracy by intelligently selecting the examples to be labeled. However, very little comparison exists between different active learning methods. The effects of the ratio of positive to negative examples on the accuracy of such algorithms also received very little attention. This paper presents a comparison of two most promising methods and their performance on a range of categories from the Reuters Corpus Vol. 1 news article dataset.

Keywords

Active Learning Version Space News Article Language Resource Active Learning Method 
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 Berlin · Heidelberg 2006

Authors and Affiliations

  • Blaž Novak
    • 1
  • Dunja Mladenič
    • 1
  • Marko Grobelnik
    • 1
  1. 1.Jožef Stefan InstituteLjubljanaSlovenia

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