@inproceedings{gamallo-2018-citiusnlp,
    title = "{C}itius{NLP} at {S}em{E}val-2018 Task 10: The Use of Transparent Distributional Models and Salient Contexts to Discriminate Word Attributes",
    author = "Gamallo, Pablo",
    editor = "Apidianaki, Marianna  and
      Mohammad, Saif M.  and
      May, Jonathan  and
      Shutova, Ekaterina  and
      Bethard, Steven  and
      Carpuat, Marine",
    booktitle = "Proceedings of the 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Ingest-2025-COMPUTEL/S18-1156/",
    doi = "10.18653/v1/S18-1156",
    pages = "953--957",
    abstract = "This article describes the unsupervised strategy submitted by the CitiusNLP team to the SemEval 2018 Task 10, a task which consists of predict whether a word is a discriminative attribute between two other words. Our strategy relies on the correspondence between discriminative attributes and relevant contexts of a word. More precisely, the method uses transparent distributional models to extract salient contexts of words which are identified as discriminative attributes. The system performance reaches about 70{\%} accuracy when it is applied on the development dataset, but its accuracy goes down (63{\%}) on the official test dataset."
}