Home > Research > Publications & Outputs > Understanding the role of linguistic distributi...

Electronic data

  • Wingfield_Connell_corpus_v19

    Rights statement: This is an Accepted Manuscript of an article published by Taylor & Francis in Language, Cognition and Neuroscience on 20/05/2022, available online: http://www.tandfonline.com/10.1080/23273798.2022.2069278

    Accepted author manuscript, 19.1 MB, PDF document

    Embargo ends: 20/05/23

    Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License

Text available via DOI:

View graph of relations

Understanding the role of linguistic distributional knowledge in cognition

Research output: Contribution to Journal/MagazineJournal articlepeer-review

E-pub ahead of print
<mark>Journal publication date</mark>20/05/2022
<mark>Journal</mark>Language, Cognition and Neuroscience
Number of pages51
Publication StatusE-pub ahead of print
Early online date20/05/22
<mark>Original language</mark>English

Abstract

The distributional pattern of words in language forms the basis of linguistic distributional knowledge and contributes to conceptual processing, yet many questions remain regarding its role in cognition. We propose that corpus-based linguistic distributional models can represent a cognitively plausible approach to understanding linguistic distributional knowledge when assumed to represent an essential component of semantics, when trained on corpora representative of human language experience, and when they capture the diverse distributional relations that are useful to cognition. Using an extensive set of cognitive tasks that vary in the complexity of conceptual processing required, we systematically evaluate a wide range of model families, corpora, and parameters, and demonstrate that there is no one-size-fits-all approach for how linguistic distributional knowledge is used across cognition. Rather, linguistic distributional knowledge is a rich source of information about the world that can be accessed flexibly according to the conceptual complexity of the task at hand.

Bibliographic note

This is an Accepted Manuscript of an article published by Taylor & Francis in Language, Cognition and Neuroscience on 20/05/2022, available online: http://www.tandfonline.com/10.1080/23273798.2022.2069278