Sering, K., Milin, P. orcid.org/0000-0001-9708-7031 and Baayen, R.H. (2018) Language comprehension as a multi-label classification problem. Statistica Neerlandica, 72 (3). pp. 339-353. ISSN 0039-0402
Abstract
The initial stage of language comprehension is a multi-label classification problem. Listeners or readers, presented with an utterance, need to discriminate between the intended words and the tens of thousands of other words they know. We propose to address this problem by pairing a network trained with the learning rule of Rescorla andWagner (1972) with a second network trained independently with the learning rule of Widrow and Hoff (1960). The first network has to recover from sublexical input features the meanings encoded in the language signal, resulting in a vector of activations over the lexicon. The second network takes this vector as input and further reduces uncertainty about the intended message. Classification performance for a lexicon with 52,000 entries is good. The model also correctly predicts several aspects of human language comprehension. By rejecting the traditional linguistic assumption that language is a (de)compositional system, and by instead espousing a discriminative approach (Ramscar, 2013), a more parsimonious yet highly effective functional characterization of the initial stage of language comprehension is obtained.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 The Authors. Statistica Neerlandica © 2018 VVS. This is an author produced version of a paper subsequently published in Statistica Neerlandica. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | multi-label classification; language comprehension; error-driven learning; Rescorla-Wagner; Widrow-Hoff |
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) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 12 Jan 2018 13:11 |
Last Modified: | 14 Aug 2020 14:49 |
Published Version: | https://doi.org/10.1111/stan.12134 |
Status: | Published |
Publisher: | Wiley |
Refereed: | Yes |
Identification Number: | 10.1111/stan.12134 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:126172 |