@inproceedings{aedmaa-etal-2018-combining,
    title = "Combining Abstractness and Language-specific Theoretical Indicators for Detecting Non-Literal Usage of {E}stonian Particle Verbs",
    author = {Aedmaa, Eleri  and
      K{\"o}per, Maximilian  and
      Schulte im Walde, Sabine},
    editor = "Cordeiro, Silvio Ricardo  and
      Oraby, Shereen  and
      Pavalanathan, Umashanthi  and
      Rim, Kyeongmin",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Student Research Workshop",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/icon-24-ingestion/N18-4002/",
    doi = "10.18653/v1/N18-4002",
    pages = "9--16",
    abstract = "This paper presents two novel datasets and a random-forest classifier to automatically predict literal vs. non-literal language usage for a highly frequent type of multi-word expression in a low-resource language, i.e., Estonian. We demonstrate the value of language-specific indicators induced from theoretical linguistic research, which outperform a high majority baseline when combined with language-independent features of non-literal language (such as abstractness)."
}