Milin, P. orcid.org/0000-0001-9708-7031, Divjak, D., Dimitrijević, S. et al. (1 more author) (2016) Towards cognitively plausible data science in language research. Cognitive Linguistics, 27 (4). pp. 507-526. ISSN 0936-5907
Abstract
Over the past 10 years, Cognitive Linguistics has taken a Quantitative Turn. Yet, concerns have been raised that this preoccupation with quantification and modelling may not bring us any closer to understanding how language works. We show that this objection is unfounded, especially if we rely on modelling techniques based on biologically and psychologically plausible learning algorithms. These make it possible to take a quantitative approach, while generating and testing specific hypotheses that will advance our understanding of how knowledge of language emerges from exposure to usage.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2016 De Gruyter |
Keywords: | naive discrimination learning; memory-based learning; lexical decision; Serbian |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Social Sciences (Sheffield) > Department of Journalism Studies (Sheffield) |
Funding Information: | Funder Grant number BRITISH ACADEMY (THE) MD140023 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 08 Aug 2016 14:26 |
Last Modified: | 14 Nov 2017 12:03 |
Published Version: | http://dx.doi.org/10.1515/cog-2016-0055 |
Status: | Published |
Publisher: | De Gruyter |
Refereed: | Yes |
Identification Number: | 10.1515/cog-2016-0055 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:103471 |