Weighted Constraints in Generative Linguistics
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)...
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| Veröffentlicht in: | Cognitive science 2009-08, Vol.33 (6), p.999-1035 |
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| description | 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. |
| doi_str_mv | 10.1111/j.1551-6709.2009.01047.x |
| format | Article |
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| subjects | Cognition & reasoning Connectionism Generative and transformational grammars Generative linguistics Global theories Harmonic grammar Information processing Language learning Language typology Linguistic theory Linguistics Optimality theory Phonology Statistical learning Statistical methods Theory |
| title | Weighted Constraints in Generative Linguistics |
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