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- Christopher Potts, Joe Pater, +4 authors Shigeto Kawahara
- 2009

Harmonic Grammar (HG) is a model of linguistic constraint interaction in which well-formedness is calculated as the sum of weighted constraint violations. We show how linear programming algorithms can be used to determine whether there is a weighting for a set of constraints that fits a set of linguistic data. The associated software package OT-Help… (More)

- Karen Jesney, Anne-Michelle Tessier, +6 authors Matt Wolf
- 2009

In the Optimality-Theoretic learnability and acquisition literature it has been proposed that certain classes of constraints must be biased toward particular rankings (e.g., Markedness >> IO-Faithfulness; Specific IO-Faithfulness >> General IO-Faithfulness). While sometimes difficult to implement efficiently or comprehensively, these biases are necessary to… (More)

- Michael Becker, Nihan Ketrez, +8 authors Shigeto Kawahara
- 2010

In an experimental task with novel words, we find that some lexical statistical regularities of Turkish phonotactics are productively extended in nonce words, while others are not. In particular, while laryngeal alternation rates in the lexicon can be predicted by the place of articulation of the stem-final stop, by word-length, and by the preceding vowel… (More)

- Karen Jesney
- 2004

2 1. Introduction Foreign accent, as an identifying aspect of nonnative speech, has been widely discussed in both the theoretical and pedagogical literature. Still, the elements that contribute to the perception of foreign accent, and, indeed, the objective characteristics of it, remain ill defined. With this in mind, numerous studies have sought to… (More)

- Joe Pater, Christopher Potts, +6 authors Jason Riggle
- 2006

We show that Harmonic Grammars (HGs) translate into linear systems and are thus solvable using the simplex algorithm, an efficient, widely-deployed optimization algorithm that is guaranteed to deliver the optimal solution if there is one and to detect when no solution exists. Our associated software package HaLP provides a practical tool for studying even… (More)

- Anne-Michelle Tessier, Michael Becker, +8 authors Shelley Velleman
- 2008

This paper provides two arguments that constraint-based grammars should not be learned by directly mirroring the frequency of constraint violation and satisfaction in the target words of a language. The first argument comes from a class of stages attested in phonological development, called Intermediate Faith (IF) stages, in which children produce marked… (More)

- Toni Borowsky, Shigeto Kawahara, +7 authors Patrick Pratt
- 2010

This paper introduces serial Harmonic Grammar, a version of Optimality Theory (OT; Prince and Smolensky 1993/2004) that reverses two of Prince and Smolensky's basic architectural decisions. 1 One is their choice of constraint ranking over the numerically weighted constraints of its predecessor, Harmonic Grammar (HG; Legendre, Miyata and Smolensky 1990; see… (More)

- Muhammad Abdurrahman, Anisa Schardl, +9 authors Avivit Ben-David
- 2008

1. Overview This paper investigates a class of restrictive intermediate stages that emerge during L1 phonological acquisition, and argues that these stages are naturally accounted for within a gradual learning model that uses weighted constraints. The particular type of pattern of interest here – Intermediate Faithfulness (IF) stages – involves the… (More)

- Karen Jesney, Anne-Michelle Tessier, +7 authors Matt Wolf
- 2007

1. Introduction In the Optimality Theoretic learnability and acquisition literature it has been frequently argued that certain classes of constraints must be biased toward high ranking. These biases have two primary motivations: allowing the restrictive intermediate stages attested in acquisition to emerge, and ensuring that restrictive final-state grammars… (More)

- Joe Pater, Robert Staubs, Karen Jesney, Brian Smith
- SIGMORPHON
- 2012

We show that a class of cases that has been previously studied in terms of learning of abstract phonological underlying representations (URs) can be handled by a learner that chooses URs from a contextually conditioned distribution over observed surface representations. We implement such a learner in a Maximum Entropy version of Optimality Theory, in which… (More)