@inproceedings{nandakumar-etal-2018-comparative,
    title = "A Comparative Study of Embedding Models in Predicting the Compositionality of Multiword Expressions",
    author = "Nandakumar, Navnita  and
      Salehi, Bahar  and
      Baldwin, Timothy",
    editor = "Kim, Sunghwan Mac  and
      Zhang, Xiuzhen (Jenny)",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2018",
    month = dec,
    year = "2018",
    address = "Dunedin, New Zealand",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/U18-1009/",
    pages = "71--76",
    abstract = "In this paper, we perform a comparative evaluation of off-the-shelf embedding models over the task of compositionality prediction of multiword expressions(``MWEs''). Our experimental results suggest that character- and document-level models capture knowledge of MWE compositionality and are effective in modelling varying levels of compositionality, with the advantage over word-level models that they do not require token-level identification of MWEs in the training corpus."
}