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DRPS : Course Catalogue : School of Philosophy, Psychology and Language Sciences : Language Sciences

Postgraduate Course: Computational Phonology (LASC11118)

Course Outline
SchoolSchool of Philosophy, Psychology and Language Sciences CollegeCollege of Humanities and Social Science
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityNot available to visiting students
SCQF Credits20 ECTS Credits10
SummaryIn MSc Foundation: Phonology & Phonetics, students become familiar with the ways in which phonological processes affect the shapes of morphemes in particular languages. They also practice analytical and descriptive skills in dealing with morphophonological data.

Computational Phonology considers these tasks from a computational perspective. Major themes include the formalisation of representational units and analysis procedures; adjudication between competing analyses; and the induction of phonological representations.

Specific topics will include finite-state and probabilistic methods, phonotactics & gradient acceptability, syllabification, and learning of hidden structure.

Some prior programming experience (using any language) is recommended, but not strictly required. Assessment will consist of coursework (homeworks and tutorial exercises), weekly reading summaries, and a final take-home project.

In addition, students will be responsible for leading discussion on at least one paper over the course of the semester.

Formative feedback available:
- students will give short presentations about their project proposal and will receive feedback thereafter
Course description Not entered
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements None
Course Delivery Information
Not being delivered
Learning Outcomes
An overview of the use of computational models in phonological analysis.

An ability to read and assess literature in computational phonology and present the results to a peer audience.

An understanding of the fundamentals of probability theory and finite-state methods.

An ability to apply computational methods in (morpho)phonological analysis.
Reading List
Boersma, P. & Hayes, B. (2001). Empirical tests of the Gradual Learning Algorithm. Linguistic Inquiry, 32, 45¿86.
Coleman, J. & Pierrehumbert, J. (1997). Stochastic phonological grammars and acceptability. In Computational Phonology: ACL SIGPHON 3
(pp. 49¿56). Somerset, NJ: ACL.
Daelemans, W., Berck, P., & Gillis, S. (1996). Unsupervised discovery of phonological categories through supervised learning of
morphological rules. In Proceedings of the 16th conference on Computational linguistics (COLING 96), volume 1 (pp. 95¿100).
Gildea, D. & Jurafsky, D. (1995). Automatic induction of finite-state transducers for simple phonological rules. In Proceedings of the 33rd
annual meeting of the Association for Computational Linguistics (pp. 9¿15).
Goldsmith, J. (2001). Phonology as information minimization. Phonological Studies, 5, 21¿46.
Goldsmith, J. (2007). Probability for linguists. Mathematiques et sciences humaines, 180(4), 73¿98.
Goldsmith, J. & Xanthos, A. (2009). Learning phonological categories. Language, 85(1), 4¿38.
Goldwater, S. & Johnson, M. (2003). Learning OT constraints using a maximum entropy model. In J. Spenader, A. Eriksson, & Ö. Dahl
(Eds.), Proceedings of the Stockholm Workshop on Variation within Optimality Theory (pp. 111¿120).
Hayes, B. & Wilson, C. (2008). A maximum entropy model of phonotactics and phonotactic learning. Linguistic Inquiry, 39, 379¿440.
Jurafsky, D. & Martin, J. H. (2008). Speech and Language Processing (2nd Edition). Prentice Hall.
Keller, F. & Asudeh, A. (2002). Probabilistic learning algorithms and Optimality Theory. Linguistic Inquiry, 33(2), 225¿244.
Peperkamp, S., Calvez, R. L., Nadal, J.-P., & Dupoux, E. (2006). The acquisition of allophonic rules: Statistical learning with linguistic
constraints. Cognition, 101, B31¿B41.
Pierrehumbert, J. (2003). Probabilistic phonology: discrimination and robustness. In R. Bod, J. Haye, & S. Jannedy (Eds.), Probabilistic
Linguistics (pp. 177¿228). Cambridge: The MIT Press.
Additional Information
Graduate Attributes and Skills Independent and analytical thought; problem analysis and complex problem solving; clarity of expression; preparation and public presentation; experience with computational and data-processing tools and methods.
KeywordsNot entered
Contacts
Course organiserDr James Kirby
Tel: (0131 6)50 3952
Email: jkirby1@exseed.ed.ac.uk
Course secretaryMiss Toni Noble
Tel: (0131 6)51 3188
Email: Toni.noble@ed.ac.uk
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