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Browsing Ph.D. Theses by Subject "Natural language processing (Computer science)"

Browsing Ph.D. Theses by Subject "Natural language processing (Computer science)"

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  • Computer processing of Turkish: |morphological and lexical investigation 
    Güngör, Tunga. (Thesis (Ph.D.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 1995., 1995.)
    The morphological analysis of Turkish is the subject of this thesis. Turkish belongs to the group of agglutinative languages. Because of its agglutinative nature, Turkish morphology is quite complex and includes many ...
  • Deep learning-based dependency parsing for Turkish 
    Bilgin, Şaziye Betül. (Thesis (Ph.D.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2022., 2022)
    Dependency parsing is an important step for many natural language processing (NLP) systems such as question answering and machine translation. Turkish, being a morphologically rich language and having a complex grammar, ...
  • Developing a comprehensive framework for sentiment analysis in Turkish 
    Aydın, Cem Rıfkı. (Thesis (Ph.D.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2020., 2020.)
    In this thesis, we developed a comprehensive framework for sentiment analysis that takes its many aspects into account mainly for Turkish. We have also proposed several approaches specific to sentiment analysis in English ...
  • Neural named entity recognition for morphologically rich languages 
    Güngör, Onur. (Thesis (Ph.D.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2021., 2021.)
    Named entity recognition (NER) is an important task in natural language pro cessing (NLP). Until the revival of neural network based models for NLP, NER taggers employed traditional machine learning approaches or finite-state ...
  • Utilizing weakly-supervised learning for hashtag segmentation and named entity disambiguation 
    Çelebi, Arda. (Thesis (Ph.D.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2020., 2020.)
    Today’s high-performing machine learning algorithms learn to predict by the supervision of large amounts of human-labeled data. However, the labeling process is costly in terms of time and effort. In this thesis, we design ...

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