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Improving Supervised Classification Using Information Extraction

  • Mian Du
  • Matthew Pierce
  • Lidia Pivovarova
  • Roman Yangarber
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9103)

Abstract

We explore supervised learning for multi-class, multi-label text classification, focusing on real-world settings, where the distribution of labels changes dynamically over time. We use the PULS Information Extraction system to collect information about the distribution of class labels over named entities found in text. We then combine a knowledge-based rote classifier with statistical classifiers to obtain better performance than either classification method alone. The resulting classifier yields a significant improvement in macro-averaged F-measure compared to the state of the art, while maintaining comparable micro-average.

Keywords

Statistical Classifier Information Extraction Feature Selection Method Industry Sector Name Entity Recognition 
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 International Publishing Switzerland 2015

Authors and Affiliations

  • Mian Du
    • 1
  • Matthew Pierce
    • 1
  • Lidia Pivovarova
    • 1
  • Roman Yangarber
    • 1
  1. 1.Department of Computer ScienceUniversity of HelsinkiHelsinkiFinland

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