Harmonic Grammar, Gradual Learning, and Phonological Gradience
نویسنده
چکیده
(1) i. HG is (perhaps surprisingly) restrictive, due to inherent limitations on the types of languages that can be generated by an optimization system (Bhatt et al. 2007; Pater et al. 2007) ii. HG is compatible with a simple correctly convergent gradual learning algorithm, the Perceptron algorithm of Rosenblatt (1958) (Boersma and Pater 2007; Pater 2007a; see Jäger 2006, Soderstrom et al. 2006 for precedents). iii. To deal with variation, HG can be implemented with noise, as in stochastic OT (Boersma 1998; Boersma and Hayes 2001). Testing shows that noisy HG+Perceptron is robust, unlike stochastic OT+GLA (Boersma and Pater 2007). iv. Gradual learning yields Harmony values that reflect frequency distributions. A problem for the HG account of gradient well-formedness (Keller 2006; Legendre et al. 2006a) raised by Boersma (2004) can be resolved with a revised HG acceptability metric (Coetzee and Pater 2007).
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