The University of Massachusetts Amherst

2. Models of variation and learning

Weighted constraint models of grammar have received considerable attention as models of variation and models of learning. Stochastic versions of HG include Maximum Entropy Grammar and Noisy HG. Both of these models can be learned with a gradual learning algorithm that can be used for the modeling of human language acquisition. These probabilistic models with gradual learning algorithms lend themselves naturally to modeling language change in terms of agent-based or iterative learning, especially given an “emergent simplicity bias” (Pater and Moreton in prep.).

Methods: HG in R

Models of variation

Main readings for class discussion

Coetzee, Andries and Joe Pater. To appear. The place of variation in phonological theory. To appear in John Goldsmith, Jason Riggle, and Alan Yu (eds.), The Handbook of Phonological Theory (2nd ed.). Blackwell.

Goldwater, Sharon and Mark Johnson.2003. Learning OT constraint rankings using a Maximum Entropy model. Proceedings of the Workshop on Variation within Optimality Theory, Stockholm University.

Bruce Hayes, Kie Zuraw, Péter Siptár, and Zsuzsa Londe. 2009. Natural and Unnatural Constraints in Hungarian Vowel HarmonyLanguage 85. 822-863.

Johnson, Mark. 2007. A gentle introduction to Maximum Entropy Models and their friends. Slides from presentation to the Northeast Computational Phonology Circle, University of Massachusetts Amherst, November.

Models of learning

Main readings for class discussion

Boersma, Paul and Joe Pater. 2008. Convergence Properties of a Gradual Learning Algorithm for Harmonic Grammar. Ms., University of Amsterdam and University of Massachusetts, Amherst.

Farris-Trimble, A. 2008. Cumulative faithfulness effects in phonology. PhD thesis, Indiana University.

Gaja Jarosz. 2010. Implicational Markedness and Frequency in Constraint-Based Computational Models of Phonological Learning. In Journal of Child Language 37(3), Special Issue on Computational models of child language learning, 565-606. Cambridge: Cambridge University Press.

Jesney, Karen, and Anne-Michelle Tessier. 2011. Biases in Harmonic Grammar: The road to restrictive learning. Natural Language and Linguistic Theory 29. 251-290.

Further readings

Jaeger, Gerhard. 2007. Maximum Entropy Models and Stochastic Optimality Theory, in Annie Zaenen, Jane Simpson, Tracy Holloway King, Jane Grimshaw, Joan Maling, and Chris Manning (eds.), Architectures, Rules, and Preferences. Variations on Themes by Joan W. Bresnan, CSLI Publications, Stanford, 467-479.

Modeling language change

Background readings

Wedel, Andy. 2011. Self-organization in Phonology. In Marc van Oostendorp, Colin J. Ewen, Elizabeth A. Hume and Keren Rice (eds.) The Blackwell Companion to Phonology. 130-147.
Zuraw, Kie. 2003. Probability in language change. In Rens Bod, Jennifer Hay, Stefanie Jannedy, editors, Probabilistic Linguistics. Cambridge, MA: MIT Press. Pp. 139-176.

Paper discussed in class

Pater, Joe and Elliott Moreton. In prep. Simplicity bias in phonological learning.

Leave a Reply

Your email address will not be published. Required fields are marked *