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Yazar: Solak, Ercan × Konu: Natural language processing systems ×

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Listeleniyor 1 - 5 / 5
  • Küçük Resim
    Yayın
    Visual modeling of Turkish morphology
    (European Language Resources Association (ELRA), 2020-05-16) Özenç, Berke; Solak, Ercan
    In this paper, we describe the steps in a visual modeling of Turkish morphology using diagramming tools. We aimed to make modeling easier and more maintainable while automating much of the code generation. We released the resulting analyzer, MorTur, and the diagram conversion tool, DiaMor as free, open-source utilities. MorTur analyzer is also publicly available on its web page as a web service. MorTur and DiaMor are part of our ongoing efforts in building a set of natural language processing tools for Turkic languages under a consistent framework.
  • Küçük Resim
    Yayın
    Kural bazlı otomatik haber etiketleme
    (IEEE, 2017-06-27) Özenç, Berke; Solak, Ercan
    Bu çalışmada , genel ağ kaynaklarından haber toplayan ve topladığı bu haberleri otomatik olarak etiketleyen kural tabanlı bir uygulama yapılmıştır. Çalışmanın alt amacı hangi özelliklerin etiket belirleme işine daha uygun olduğunu ölçmektir. Elle etiketlenmiş 100 haber üzerinde her bir kuralın başarısı oranı ölçülmüştür.
  • Küçük Resim
    Yayın
    MorAz: An open-source morphological analyzer for Azerbaijani Turkish
    (Association for Computational Linguistics (ACL), 2018) Özenç, Berke; Ehsani, Razieh; Solak, Ercan
    MorAz is an open-source morphological analyzer for Azerbaijani Turkish. The analyzer is available through both as a website for interactive exploration and as a RESTful web service for integration into a natural language processing pipeline. MorAz implements the morphology of Azerbaijani Turkish following a two-level approach using Helsinki finite-state transducer and wraps the analyzer with python scripts in a Django instance.
  • Küçük Resim
    Yayın
    Chunking in Turkish with conditional random fields
    (Springer-Verlag, 2015-04-14) Yıldız, Olcay Taner; Solak, Ercan; Ehsani, Razieh; Görgün, Onur
    In this paper, we report our work on chunking in Turkish. We used the data that we generated by manually translating a subset of the Penn Treebank. We exploited the already available tags in the trees to automatically identify and label chunks in their Turkish translations. We used conditional random fields (CRF) to train a model over the annotated data. We report our results on different levels of chunk resolution.
  • Küçük Resim
    Yayın
    A FST description of noun and verb morphology of Azarbaijani Turkish
    (Association for Computational Linguistics (ACL), 2021) Ehsani, Razieh; Özenç, Berke; Solak, Ercan; Drewes F.
    We give a FST description of nominal and finite verb morphology of Azarbaijani Turkish. We use a hybrid approach where nominal inflection is expressed as a slot-based paradigm and major parts of verb inflection are expressed as optional paths on the FST. We collapse adjective and noun categories in a single nominal category as they behave similarly as far as their paradigms are concerned. Thus, we defer a more precise identification of POS to further down the NLP pipeline.