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Getting the Most out of Transition-based Dependency Parsing

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Jinho Choi
Jinho Choi

This document summarizes research on improving transition-based dependency parsing using a bootstrapping technique. It presents the parsing algorithm, experimental setup evaluating models on English and Czech data, and results showing the bootstrapping approach achieves near state-of-the-art performance while maintaining linear parsing time complexity. It also introduces an open-source parser implementation called ClearParser.

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Getting the Most out of Transition-based Dependency ParsingJinho D. Choi and Martha PalmerInstitute of Cognitive Science, University of Colorado at Boulder Parsing Algorithm Experiments Introduction Bootstrapping Technique Experimental setup ,[object Object]

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Getting the Most out of Transition-based Dependency Parsing

  • 1.
  • 2.
  • 3. Our+: ‘Our’ + bootstrapping technique.
  • 4. Gesmundo et al.: the best transition-based system for CoNLL’09.
  • 5. Bohnet: the best graph-based system for CoNLL’09 (the overallrank is in the parenthesis).
  • 6.
  • 7. ‘Nivre’ indicates Nivre’s swap algorithm that showed an expected linear time non-projective parsing complexity (Nivre, 2009), of which we used the implementation from MaltParser.
  • 8.
  • 9. L is a dependency label, and i, j, k are indices of their corresponding word tokens.
  • 10. The initial state is ([0], [ ], [1, …, n], E); 0 corresponds to the root node.
  • 11. The final state is (λ1, λ2, [ ], E); the algorithm terminates when all tokens in β are consumed.
  • 12. Left-PopL and Left-ArcL are performed when wj is the head of wi with a dependency L. : Left-Pop removes wi from λ1, assuming that the token is no longer needed. : Left-Arc keepswiso it can be the head of some token wj<k≤n in β.
  • 13. Right-ArcL is performed when wi is the head of wj with a dependency L.
  • 14. Shift is performed when : DT – λ1is empty. : NT – There is no token in λ1 that is either the head or a dependent of wj.
  • 15.
  • 16.
  • 17. Parse history can be used as features. : Parsing complexity is still preserved. Can non-projective dependency parsing be any faster?
  • 18. # of non-projective dependencies <<< # of projective dependencies. : Perform projective parsing for most cases and non-projective parsing only when it is needed.
  • 19. Choi and Nicolov, 2009. : Added a non-deterministic Shifttransition to Nivre’s list-based non-projective algorithmreduced the search space achieved linear time parsing speed in practice.
  • 20.
  • 21.
  • 24.
  • 25.
  • 26. Contact: Jinho D. Choi (choijd@colorado.edu)
  • 27.
  • 28. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.
  • 29. Stop the procedure when the parsing accuracy of the current cross-validation is lower than the one from the previous iteration.