@inproceedings{ebert-pavlick-2019-using,
    title = "Using Grounded Word Representations to Study Theories of Lexical Concepts",
    author = "Ebert, Dylan  and
      Pavlick, Ellie",
    editor = "Chersoni, Emmanuele  and
      Jacobs, Cassandra  and
      Lenci, Alessandro  and
      Linzen, Tal  and
      Pr{\'e}vot, Laurent  and
      Santus, Enrico",
    booktitle = "Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/W19-2918/",
    doi = "10.18653/v1/W19-2918",
    pages = "160--169",
    abstract = "The fields of cognitive science and philosophy have proposed many different theories for how humans represent ``concepts''. Multiple such theories are compatible with state-of-the-art NLP methods, and could in principle be operationalized using neural networks. We focus on two particularly prominent theories{--}Classical Theory and Prototype Theory{--}in the context of visually-grounded lexical representations. We compare when and how the behavior of models based on these theories differs in terms of categorization and entailment tasks. Our preliminary results suggest that Classical-based representations perform better for entailment and Prototype-based representations perform better for categorization. We discuss plans for additional experiments needed to confirm these initial observations."
}