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      • Bilkent Theses
      • Theses - Department of Computer Engineering
      • Dept. of Computer Engineering - Master's degree
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      Noun phrase chunker for Turkish using dependency parser

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      Author
      Kutlu, Mücahid
      Advisor
      Ulusoy, Özgür
      Date
      2010
      Publisher
      Bilkent University
      Language
      English
      Type
      Thesis
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      Abstract
      Noun phrase chunking is a sub-category of shallow parsing that can be used for many natural language processing tasks. In this thesis, we propose a noun phrase chunker system for Turkish texts. We use a weighted constraint dependency parser to represent the relationship between sentence components and to determine noun phrases. The dependency parser uses a set of hand-crafted rules which can combine morphological and semantic information for constraints. The rules are suitable for handling complex noun phrase structures because of their flexibility. The developed dependency parser can be easily used for shallow parsing of all phrase types by changing the employed rule set. The lack of reliable human tagged datasets is a significant problem for natural language studies about Turkish. Therefore, we constructed the first noun phrase dataset for Turkish. According to our evaluation results, our noun phrase chunker gives promising results on this dataset. The correct morphological disambiguation of words is required for the correctness of the dependency parser. Therefore, in this thesis, we propose a hybrid morphological disambiguation technique which combines statistical information, hand-crafted grammar rules, and transformation based learning rules. We have also constructed a dataset for testing the performance of our disambiguation system. According to tests, the disambiguation system is highly effective.
      Keywords
      Natural Language Processing
      Noun Phrase Chunker
      Turkish
      Shallow Parsing
      Morphological Disambiguation
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      http://hdl.handle.net/11693/15149
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      • Dept. of Computer Engineering - Master's degree 511
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