Exploiting subjectivity classification to improve information extraction

  • Authors:

  • Ellen Riloff;Janyce Wiebe;William Phillips

  • Affiliations:

  • School of Computing, University of Utah, Salt Lake City, UT;Department of Computer Science, University of Pittsburgh, Pittsburgh, PA;School of Computing, University of Utah, Salt Lake City, UT

  • Venue:
  • AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
  • Year:
  • 2005

Quantified Score

Hi-index 0.00

Visualization

Abstract

Information extraction (IE) systems are prone to false hits for a variety of reasons and we observed that many of these false hi ts occur in sentences that contain subjective language (e.g., opinions, emotions, and sentiments). Motivated by these observations, we explore the idea of using subjectivity analysis to improve the precision of information extraction systems. In this paper, we describe an IE system that uses a subjective sentence classifier to filter its extractions. We experimented with several different strategies for using the subjectivity classifications, including an aggressive strategy that discards all extractions found in subjective sentences and more complex strategies that selectively discard extractions. We evaluated the performance of these different approaches on the MUC-4 terrorism data set. We found that indiscriminately filtering extractions from subjective sentences was overly aggressive, but more selective filtering strategies improved IE precision with minimal recall loss.