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Part of
Crossroads Semantics: Computation, experiment and grammar
Edited by Hilke Reckman, Lisa Lai-Shen Cheng, Maarten Hijzelendoorn and Rint Sybesma
[Not in series 210] 2017
► pp. 57–76

Chapter 4
How to compare speed and accuracy of syntactic parsers

| University of Groningen

The paper introduces a methodological innovation as well as a practical innovation. Firstly, two scenarios are introduced to compare accurate, but slow parsers on the one hand, with faster, but less accurate parsers on the other hand. Secondly, a corpus-based technique is described to improve the efficiency of wide-coverage high-accuracy parsers. By keeping track of the derivation steps which lead to the best parse for a very large collection of sentences, the parser learns which parse steps can be filtered without significant loss in parsing accuracy, but with an important increase in parsing efficiency. Experimental results with the Alpino parser for Dutch indicate that the technique yields much faster parsers that perform with almost the same level of accuracy. An interesting characteristic of our approach is that it is self-learning, in the sense that it uses unannotated corpora.

Keywords: Parsing, Alpino, Dutch, Efficiency, Accuracy, HPSG
Article outline
  • 1.Introduction
  • 2.Background: The Alpino parser for Dutch
  • 3.Methodology: Balancing efficiency and accuracy
    • 3.1On-line and off-line parsing scenarios
      • 3.1.1On-line scenario
      • 3.1.2Off-line scenario
    • 3.2Accuracy: Comparing sets of dependencies
  • 4.Learning efficient parsing
    • 4.1Left-corner parsing
    • 4.2Left-corner splines
    • 4.3Filtering left-corner splines
      • 4.3.1Context size
      • 4.3.2Required evidence
    • 4.4Comparison with link table
    • 4.5Implementation detail
  • 5.Experimental results
    • 5.1Results on Alpino Treebank
    • 5.2Effect of the amount of training data
    • 5.3Experiment with D-Coi data
  • 6.Specializing lexical categories
  • 7.Discussion
  • Acknowledgements
  • Note
  • References
Published online: 12 April 2017
DOI logo
https://doi.org/10.1075/z.210.04van
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