The Effect of Semi-supervised Learning on Parsing Long Distance Dependencies in German and Swedish

  • Anders Søgaard
  • Christian Rishøj
Conference paper

DOI: 10.1007/978-3-642-14770-8_44

Part of the Lecture Notes in Computer Science book series (LNCS, volume 6233)
Cite this paper as:
Søgaard A., Rishøj C. (2010) The Effect of Semi-supervised Learning on Parsing Long Distance Dependencies in German and Swedish. In: Loftsson H., Rögnvaldsson E., Helgadóttir S. (eds) Advances in Natural Language Processing. NLP 2010. Lecture Notes in Computer Science, vol 6233. Springer, Berlin, Heidelberg

Abstract

This paper shows how the best data-driven dependency parsers available today [1] can be improved by learning from unlabeled data. We focus on German and Swedish and show that labeled attachment scores improve by 1.5%-2.5%. Error analysis shows that improvements are primarily due to better recovery of long distance dependencies.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Anders Søgaard
    • 1
  • Christian Rishøj
    • 1
  1. 1.Center for Language TechnologyUniversity of CopenhagenCopenhagen S

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