@inproceedings{ebert-pavlick-2019-using,
title = "Using Grounded Word Representations to Study Theories of Lexical Concepts",
author = "Ebert, Dylan and
Pavlick, Ellie",
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://aclanthology.org/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.",
}
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%0 Conference Proceedings
%T Using Grounded Word Representations to Study Theories of Lexical Concepts
%A Ebert, Dylan
%A Pavlick, Ellie
%S Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics
%D 2019
%8 jun
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F ebert-pavlick-2019-using
%X 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.
%R 10.18653/v1/W19-2918
%U https://aclanthology.org/W19-2918
%U https://doi.org/10.18653/v1/W19-2918
%P 160-169
Markdown (Informal)
[Using Grounded Word Representations to Study Theories of Lexical Concepts](https://aclanthology.org/W19-2918) (Ebert & Pavlick, 2019)
ACL