Morphological Disambiguation of Turkish Text with Perceptron Algorithm

  • Haşim Sak
  • Tunga Güngör
  • Murat Saraçlar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4394)


This paper describes the application of the perceptron algorithm to the morphological disambiguation of Turkish text. Turkish has a productive derivational morphology. Due to the ambiguity caused by complex morphology, a word may have multiple morphological parses, each with a different stem or sequence of morphemes. The methodology employed is based on ranking with perceptron algorithm which has been successful in some NLP tasks in English. We use a baseline statistical trigram-based model of a previous work to enumerate an n-best list of candidate morphological parse sequences for each sentence. We then apply the perceptron algorithm to rerank the n-best list using a set of 23 features. The perceptron trained to do morphological disambiguation improves the accuracy of the baseline model from 93.61% to 96.80%. When we train the perceptron as a POS tagger, the accuracy is 98.27%. Turkish morphological disambiguation and POS tagging results that we obtained is the best reported so far.


Baseline Model Viterbi Algorithm Root Word Morphosyntactic Feature Perceptron Algorithm 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Haşim Sak
    • 1
  • Tunga Güngör
    • 1
  • Murat Saraçlar
    • 2
  1. 1.Dept. of Computer Engineering, Boğaziçi University, Bebek, 34342, IstanbulTurkey
  2. 2.Dept. of Electrical and Electronic Engineering, Boğaziçi University, Bebek, 34342, IstanbulTurkey

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