Journal article
Grammaticality, Acceptability, and Probability: A Probabilistic View of Linguistic Knowledge
JH Lau, A Clark, S Lappin
Cognitive Science | WILEY | Published : 2017
DOI: 10.1111/cogs.12414
Abstract
The question of whether humans represent grammatical knowledge as a binary condition on membership in a set of well-formed sentences, or as a probabilistic property has been the subject of debate among linguists, psychologists, and cognitive scientists for many decades. Acceptability judgments present a serious problem for both classical binary and probabilistic theories of grammaticality. These judgements are gradient in nature, and so cannot be directly accommodated in a binary formal grammar. However, it is also not possible to simply reduce acceptability to probability. The acceptability of a sentence is not the same as the likelihood of its occurrence, which is, in part, determined by f..
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Awarded by Chalmers Tekniska Högskola
Funding Acknowledgements
The research reported here was done as part of the Statistical Models of Grammar (SMOG) project at King's College London (www.dcs.kcl.ac.uk/staff/lappin/smog/), funded by grant ES/J022969/1 from the Economic and Social Research Council of the UK. We are grateful to Douglas Saddy and Garry Smith at the Centre for Integrative Neuroscience and Neurodynamics at the University of Reading for generously giving us access to their computing cluster, and for much helpful technical support. We thank J. David Lappin for invaluable assistance in organizing our AMT HITS. We presented part of the work discussed here to CL/NLP, cognitive science, and machine learning colloquia at Chalmers University of Technology, University of Gothenburg, University of Sheffield, The University of Edinburgh, The Weizmann Institute of Science, University of Toronto, MIT, and the ILLC at the University of Amsterdam. We very much appreciate the comments and criticisms that we received from these audiences, which have guided us in our research. We also thank Ben Ambridge, Jennifer Culbertson, Jeff Heinz, Greg Kobele, and Richard Sproat for helpful comments on earlier drafts of this paper. Finally, we thank two anonymous referees and the editor for their insightful suggestions and criticisms. These have been of considerable help to us in producing what we hope is an improved version of the paper. Of course, we bear sole responsibility for any errors that remain.