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Empirical Tests of the Gradual Learning Algorithm
Open accessAbstract
The Gradual Learning Algorithm (Boersma 1997) is a constraint-ranking algorithm for learning optimality-theoretic grammars. The purpose of this article is to assess the capabilities of the Gradual Learning Algorithm, particularly in comparison with the Constraint Demotion algorithm of Tesar and Smolensky (1993, 1996, 1998, 2000), which initiated the learnability research program for Optimality Theory. We argue that the Gradual Learning Algorithm has a number of special advantages: it can learn free variation, deal effectively with noisy learning data, and account for gradient well-formedness judgments. The case studies we examine involve Ilokano reduplication and metathesis, Finnish genitive plurals, and the distribution of English light and dark /l/.
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Citations
Cited by
Hopper, P. J., & Traugott, E. C. (2003) · doi:10.1017/cbo9781139165525 · Cited by 2,135
BRIDGING THE GAP BETWEEN L2 SPEECH PERCEPTION RESEARCH AND PHONOLOGICAL THEORY
Escudero, P., & Boersma, P. (2004) · Studies in Second Language Acquisition · doi:10.1017/s0272263104040021 · Cited by 459
Self-citationProbabilistic models of language processing and acquisition
Chater, N., & Manning, C. D. (2006) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2006.05.006 · Cited by 411
Natural and unnatural constraints in Hungarian vowel harmony
Hayes, B., Zuraw, K., Siptár, P., & Londe, Z. (2009) · Language · doi:10.1353/lan.0.0169 · Cited by 391
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Metadata
- Authors
- Boersma, P., & Hayes, B.
- Venue
- Linguistic Inquiry
- Publisher
- MIT Press - Journals
- Published
- 2001-01
- Volume
- 32
- Issue
- 1
- Pages
- 45-86
- ISSN
- 0024-3892 (ISSN-L)
- 1530-9150
- OpenAlex
- W2097609022
- Cited by
- 1,115
- References
- 0
- FWCI
- 51.38
- Type
- journal article
- Language
- en
- Open access
- bronze
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