The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Speakers optimize information density through syntactic reduction
Abstract
If language users are rational, they might choose to structure their utterances so as to optimize communicative properties. In particular, information-theoretic and psycholinguistic considerations suggest that this may include maximizing the uni-formity of information density in an utterance. We investigate this possibility in the context of syntactic reduction, where the speaker has the option of either marking a higher-order unit (a phrase) with an extra word, or leaving it unmarked. We demonstrate that speakers are more likely to reduce less information-dense phrases. In a second step, we combine a stochastic model of structured utterance production with a logistic-regression model of syntactic reduction to study which types of cues speakers employ when estimating the predictability of upcoming elements. We demonstrate that the trend toward predictability-sensitive syntactic reduction (Jaeger, 2006) is robust in the face of a wide variety of control vari-ables, and present evidence that speakers use both surface and structural cues for predictability estimation. 1
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Cited by
Zipf’s word frequency law in natural language: A critical review and future directions
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More than words: Frequency effects for multi-word phrases
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Redundancy and reduction: Speakers manage syntactic information density
Florian Jaeger, T. (2010) · Cognitive Psychology · doi:10.1016/j.cogpsych.2010.02.002 · Cited by 733
The effect of word predictability on reading time is logarithmic
Smith, N. J., & Levy, R. (2013) · Cognition · doi:10.1016/j.cognition.2013.02.013 · Cited by 725
Metadata
- Authors
- Levy, R., & Jaeger, T. F.
- Venue
- Advances in Neural Information Processing Systems 19
- Publisher
- The MIT Press
- Published
- 2007-09-07
- Pages
- 849-856
- OpenAlex
- W2157365695
- Cited by
- 503
- References
- 15
- FWCI
- 48.60
- Type
- book chapter
- Language
- en
- Open access
- closed
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