@inproceedings{markantonatou-etal-2025-vmwe,
    title = "{VMWE} identification with models trained on {GUD} (a {UD}v.2 treebank of Standard {M}odern {G}reek)",
    author = "Markantonatou, Stella  and
      Stamou, Vivian  and
      Bompolas, Stavros  and
      Anastasopoulou, Katerina  and
      Vasileiadi, Irianna Linardaki  and
      Diamantopoulos, Konstantinos  and
      Kazos, Yannis  and
      Anastasopoulos, Antonios",
    editor = {Ojha, Atul Kr.  and
      Giouli, Voula  and
      Mititelu, Verginica Barbu  and
      Constant, Mathieu  and
      Korvel, Gra{\v{z}}ina  and
      Do{\u{g}}ru{\"o}z, A. Seza  and
      Rademaker, Alexandre},
    booktitle = "Proceedings of the 21st Workshop on Multiword Expressions (MWE 2025)",
    month = may,
    year = "2025",
    address = "Albuquerque, New Mexico, U.S.A.",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Ingest-2025-COMPUTEL/2025.mwe-1.3/",
    pages = "14--20",
    ISBN = "979-8-89176-243-5",
    abstract = "UD{\_}Greek-GUD (GUD) is the most recent Universal Dependencies (UD) treebank for Standard Modern Greek (SMG) and the first SMG UD treebank to annotate Verbal Multiword Expressions (VMWEs). GUD contains material from fiction texts and various sites that use colloquial SMG. We describe the special annotation decisions we implemented with GUD, the pipeline we developed to facilitate the active annotation of new material, and we report on the method we designed to evaluate the performance of models trained on GUD as regards VMWE identification tasks."
}