@inproceedings{akkus-etal-2017-initial,
    title = "Initial Explorations of {CCG} Supertagging for {U}niversal {D}ependency Parsing",
    author = "Akkus, Burak Kerim  and
      Azizoglu, Heval  and
      Cakici, Ruket",
    editor = "Haji{\v{c}}, Jan  and
      Zeman, Dan",
    booktitle = "Proceedings of the {C}o{NLL} 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/K17-3023/",
    doi = "10.18653/v1/K17-3023",
    pages = "218--227",
    abstract = "In this paper we describe the system by METU team for universal dependency parsing of multilingual text. We use a neural network-based dependency parser that has a greedy transition approach to dependency parsing. CCG supertags contain rich structural information that proves useful in certain NLP tasks. We experiment with CCG supertags as additional features in our experiments. The neural network parser is trained together with dependencies and simplified CCG tags as well as other features provided."
}