A Survey of Noise Reduction Methods for Distant Supervision

Benjamin Roth, Tassilo Barth, Benjamin Roth, Dietrich Klakow

Jun 29, 2013 (modified: Jun 29, 2013) AKBC 2013 submission readers: everyone
  • Abstract: We survey recent approaches to noise reduction in distant supervision learning for relation extraction. We find that all of them are based on one of three basic principles: at-least-one constraints, topic-based models, or pattern correlations. Besides describing them, we illustrate the fundamental differences and attempt to give an outlook to potentially fruitful further research. In addition, we identify related work in sentiment analysis which could profit from approaches to noise reduction.
  • Decision: conferencePoster
  • Authorids: beroth@coli.uni-saarland.de, tbarth@lsv.uni-saarland.de, beroth@coli.uni-saarland.de, dietrich.klakow@lsv.uni-saarland.de

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