Klavan, J. and Divjak, D. (2016) The Cognitive Plausibility of Statistical Classification Models: Comparing Textual and Behavioral Evidence. Folia Linguistica, 50 (2). pp. 355-384. ISSN 0165-4004
Abstract
Usage-based linguistics abounds with studies that use statistical classification models to analyse either textual corpus data or behavioral experimental data. Yet, before we can draw conclusions from statistical models of empirical data that we can feed back into cognitive linguistic theory, we need to assess whether the text-based models are cognitively plausible and whether the behavior-based models are linguistically accurate. In this paper, we review four case studies that evaluate statistical classification models of richly annotated linguistic data by explicitly comparing the performance of a corpus-based model to the behavior of native speakers. The data come from four different languages (Arabic, English, Estonian, and Russian) and pertain to both lexical as well as syntactic near-synonymy. We show that behavioral evidence is needed in order to fine-tune and improve statistical models built on data from a corpus. We argue that methodological pluralism and triangulation are the keys for a cognitively realistic linguistic theory.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2016 De Gruyter. This is an author produced version of a paper subsequently published in Folia Linguistica. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | statistical modeling; near-synonymy; corpus linguistics; (psycho)linguistic experiments |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Arts and Humanities (Sheffield) > School of Languages and Cultures (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 02 Mar 2016 09:52 |
Last Modified: | 08 Nov 2017 01:38 |
Published Version: | https://doi.org/10.1515/flin-2016-0014 |
Status: | Published |
Publisher: | De Gruyter |
Refereed: | Yes |
Identification Number: | 10.1515/flin-2016-0014 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:95847 |