The Incremental Use of Morphological Information and Lexicalization in Data-Driven Dependency Parsing

  • Gülşen Eryiğit
  • Joakim Nivre
  • Kemal Oflazer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4285)


Typological diversity among the natural languages of the world poses interesting challenges for the models and algorithms used in syntactic parsing. In this paper, we apply a data-driven dependency parser to Turkish, a language characterized by rich morphology and flexible constituent order, and study the effect of employing varying amounts of morpholexical information on parsing performance. The investigations show that accuracy can be improved by using representations based on inflectional groups rather than word forms, confirming earlier studies. In addition, lexicalization and the use of rich morphological features are found to have a positive effect. By combining all these techniques, we obtain the highest reported accuracy for parsing the Turkish Treebank.


Word Form Morphological Information Syntactic Parsing Dependency Parsing Label Dependency 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Gülşen Eryiğit
    • 1
  • Joakim Nivre
    • 2
  • Kemal Oflazer
    • 3
  1. 1.Department of Computer EngineeringIstanbul Technical Univ.Turkey
  2. 2.School of Mathematics and Systems EngineeringVäxjö Univ.Sweden
  3. 3.Faculty of Engineering and Natural SciencesSabancı Univ.Turkey

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