The DOI matches its Crossref record; 52 of the 52 references it cites were found in a registry or an index.
Weighted Constraints in Generative Linguistics
Open accessAbstract
Harmonic Grammar (HG) and Optimality Theory (OT) are closely related formal frameworks for the study of language. In both, the structure of a given language is determined by the relative strengths of a set of constraints. They differ in how these strengths are represented: as numerical weights (HG) or as ranks (OT). Weighted constraints have advantages for the construction of accounts of language learning and other cognitive processes, partly because they allow for the adaptation of connectionist and statistical models. HG has been little studied in generative linguistics, however, largely due to influential claims that weighted constraints make incorrect predictions about the typology of natural languages, predictions that are not shared by the more popular OT. This paper makes the case that HG is in fact a promising framework for typological research, and reviews and extends the existing arguments for weighted over ranked constraints.
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Cites
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Metadata
- Authors
- Pater, J.
- Venue
- Cognitive Science
- Publisher
- Wiley
- Published
- 2009-07-23
- Volume
- 33
- Issue
- 6
- Pages
- 999-1035
- ISSN
- 0364-0213 (ISSN-L)
- 1551-6709
- OpenAlex
- W2142772987
- Cited by
- 375
- References
- 52
- FWCI
- 18.88
- Type
- journal article
- Language
- en
- Open access
- bronze
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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.
