Empirical Tests of the Gradual Learning Algorithm  

Oleh:

Hayes, Bruce ; Boersma, Paul

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: Linguistic Inquiry (ada di JSTOR) vol. 32 no. 1 (2001), page 45-86.
Fulltext: Vol 32 No 1 pp 45-86.pdf (4.66MB)

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Perpustakaan PKBB Nomor Panggil: 405/LII/32 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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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 Ill. Keywords: learnability, Optimality Theory, variation, Ilokano, Finnish

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