@inproceedings{mandravickaite-krilavicius-2017-identification,
    title = "Identification of Multiword Expressions for {L}atvian and {L}ithuanian: Hybrid Approach",
    author = "Mandravickait{\.{e}}, Justina  and
      Krilavi{\v{c}}ius, Tomas",
    editor = "Markantonatou, Stella  and
      Ramisch, Carlos  and
      Savary, Agata  and
      Vincze, Veronika",
    booktitle = "Proceedings of the 13th Workshop on Multiword Expressions ({MWE} 2017)",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/W17-1712/",
    doi = "10.18653/v1/W17-1712",
    pages = "97--101",
    abstract = "We discuss an experiment on automatic identification of bi-gram multi-word expressions in parallel Latvian and Lithuanian corpora. Raw corpora, lexical association measures (LAMs) and supervised machine learning (ML) are used due to deficit and quality of lexical resources (e.g., POS-tagger, parser) and tools. While combining LAMs with ML is rather effective for other languages, it has shown some nice results for Lithuanian and Latvian as well. Combining LAMs with ML we have achieved 92,4{\%} precision and 52,2{\%} recall for Latvian and 95,1{\%} precision and 77,8{\%} recall for Lithuanian."
}