A Maximum Entropy Model of Phonotactics and Phonotactic Learning

By Bruce Hayes, Colin Wilson. Linguistic Inquiry, 2008

735 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Experimental and Cognitive Psychology, Signal Processing

View Paper: https://www.semanticscholar.org/paper/6265c088572a876bde852c85491439a5f47150a2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Kie Zuraw
2. Bruce Hayes
3. KevinM . Ryan
4. Joe Pater
5. Jeffrey Heinz
6. Z. Londe
7. Andries W. Coetzee
8. D. C. Hall

In Experimental and Cognitive Psychology

1. Kie Zuraw
2. Joe Pater
3. Bruce Hayes
4. KevinM . Ryan
5. Jeffrey Heinz
6. Z. Londe
7. Andries W. Coetzee
8. John J. McCarthy

In Signal Processing

1. KevinM . Ryan
2. Joe Pater
3. Jeffrey Heinz

Citations in the corpus

• Natural and Unnatural Constraints in Hungarian Vowel Harmony (2009, background): “First, the underlearning of the unnatural constraints may be reflecting a naturalness bias, of the sort suggested by Wilson (2006) and Moreton (2008).”
• Weighted Constraints in Generative Linguistics (2009, background): “…sketch an account of how phonological constraints could be constructed based on phonological structures in the learning data (see Burzio, 2002; Hayes & Wilson, 2008 for related precedents), and also an account of how phonetic tendencies could become phonological constraints over the course of…”
• Gradient syllable weight and weight universals in quantitative metrics* (2011, methodology): “…capable of handling gradient variation, I employ maximum entropy (maxent) grammar (Johnson 2002, Goldwater & Johnson 2003, Wilson 2006, Jäger 2007, Hayes & Wilson 2008, Ryan 2010, Hayes & Moore-Cantwell 2011; cf. Boersma & Pater 2008), a type of Harmonic Grammar (Smolensky & Legendre 2006, Pater…”
• On the role of locality in learning stress patterns* (2009, background): “…1997, Boersma & Hayes 2001), minimum description length (Ellison 1991, Goldsmith & Riggle, ms), Bayes’ Law (Tenenbaum 1999, Goldwater 2006), maximum entropy (Goldwater & Johnson 2003, Hayes & Wilson 2008) and approaches inspired by Darwinian-like processes (Clark 1992, Yang 2000, Martin 2007).”
• Gradual Learning and Convergence (2008, background): “Keller 2000, 2006, Keller and Asudeh 2002, Coetzee and Pater 2007, Hayes and Wilson 2008; cf. Boersma 2004).”