The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Expectation-based syntactic comprehension
Open accessAbstract
This paper investigates the role of resource allocation as a source of processing difficulty in human sentence comprehension. The paper proposes a simple information-theoretic characterization of processing difficulty as the work incurred by resource reallocation during parallel, incremental, probabilistic disambiguation in sentence comprehension, and demonstrates its equivalence to the theory of Hale [Hale, J. (2001). A probabilistic Earley parser as a psycholinguistic model. In Proceedings of NAACL (Vol. 2, pp. 159-166)], in which the difficulty of a word is proportional to its surprisal (its negative log-probability) in the context within which it appears. This proposal subsumes and clarifies findings that high-constraint contexts can facilitate lexical processing, and connects these findings to well-known models of parallel constraint-based comprehension. In addition, the theory leads to a number of specific predictions about the role of expectation in syntactic comprehension, including the reversal of locality-based difficulty patterns in syntactically constrained contexts, and conditions under which increased ambiguity facilitates processing. The paper examines a range of established results bearing on these predictions, and shows that they are largely consistent with the surprisal theory.
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Cites
Building a Large Annotated Corpus of English: The Penn Treebank
Marcus, M. (1993) · doi:10.21236/ada273556 · Cited by 7,405
Distributed representations, simple recurrent networks, and grammatical structure
Elman, J. L. (1991) · Machine Learning · doi:10.1007/bf00114844 · Cited by 1,354
Contextual effects on word perception and eye movements during reading
Ehrlich, S. F., & Rayner, K. (1981) · Journal of Verbal Learning and Verbal Behavior · doi:10.1016/s0022-5371(81)90220-6 · Cited by 796
(2013) · doi:10.4324/9780203763247 · Cited by 400
Cited by
On the Opportunities and Risks of Foundation Models
Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., et al. (2021) · arXiv · doi:10.48550/arxiv.2108.07258 · Cited by 2,676
SPICE: Semantic Propositional Image Caption Evaluation
Anderson, P., Fernando, B., Johnson, M., & Gould, S. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46454-1_24 · Cited by 1,986
Advances in natural language processing
Hirschberg, J., & Manning, C. D. (2015) · Science · doi:10.1126/science.aaa8685 · Cited by 1,712
An integrated theory of language production and comprehension
Pickering, M. J., & Garrod, S. (2013) · Behavioral and Brain Sciences · doi:10.1017/s0140525x12001495 · Cited by 1,569
What do we mean by prediction in language comprehension?
Kuperberg, G. R., & Jaeger, T. F. (2016) · Language, Cognition and Neuroscience · doi:10.1080/23273798.2015.1102299 · Cited by 1,087
Metadata
- Authors
- Levy, R.
- Venue
- Cognition
- Publisher
- Elsevier BV
- Published
- 2008-03
- Volume
- 106
- Issue
- 3
- Pages
- 1126-1177
- ISSN
- 0010-0277 (ISSN-L)
- OpenAlex
- W2164418233
- Cited by
- 2,202
- References
- 4
- FWCI
- 21.50
- Type
- journal article
- Language
- en
- Open access
- gold
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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