The DOI matches its Crossref record; 120 of the 120 references it cites were found in a registry or an index.
Predicting Syntax: Processing Dative Constructions in American and Australian Varieties of English
Abstract
The present study uses probabilistic models of corpus data in a novel way, to measure and compare the syntactic predictive capacities of speakers' of different varieties of the same language. The study finds that speakers' knowledge of probabilistic grammatical choices can vary across different varieties of the same language and can be detected psycholinguistically in the individual. In three pairs of experiments, Australians and Americans responded reliably to corpus model probabilities in rating the naturalness of alternative dative constructions, their lexical-decision latencies during reading varied inversely with the syntactic probabilities of the construction, and they showed subtle covariation in these tasks, which is in line with quantitative differences in the choices of datives produced in the same contexts.
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A Mathematical Theory of Communication
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Mixed-effects modeling with crossed random effects for subjects and items
Baayen, R. H., Davidson, D. J., & Bates, D. M. (2008) · Journal of Memory and Language · doi:10.1016/j.jml.2007.12.005 · Cited by 8,540
Building a Large Annotated Corpus of English: The Penn Treebank
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Regression Diagnostics -- Identifying Influential Data and Sources of Collinearity
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Categorical data analysis: Away from ANOVAs (transformation or not) and towards logit mixed models
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“To attack this problem head-on, we make use of multilevel (also termed mixed effect) regression models, which are of growing importance in the language sciences (Baayen 2008, Johnson 2008, Baayen et al. 2008, Quené and van den Bergh 2008, Jaeger 2008).”
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Biber, D., & Conrad, S. (2019) · Cambridge University Press eBooks · doi:10.1017/9781108686136 · Cited by 623
Argument Structure in Usage-Based Construction Grammar: Experimental and corpus-based perspectives
Perek, F. (2015) · Constructional Approaches to Language · doi:10.1075/cal.17 · Cited by 417
COGS: A Compositional Generalization Challenge Based on Semantic Interpretation
Kim, N., & Linzen, T. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.731 · Cited by 153
Listener sensitivity to probabilistic conditioning of sociolinguistic variables: The case of (ING)
Vaughn, C., & Kendall, T. (2018) · Journal of Memory and Language · doi:10.1016/j.jml.2018.07.006 · Cited by 17
Metadata
- Authors
- Bresnan, J., & Ford, M.
- Venue
- Language
- Publisher
- Cambridge University Press (CUP)
- Published
- 2010-03
- Volume
- 86
- Issue
- 1
- Pages
- 168-213
- ISSN
- 0097-8507 (ISSN-L)
- 1535-0665
- OpenAlex
- W1969043111
- Cited by
- 613
- References
- 120
- FWCI
- 152.71
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
