Postgraduate Course: Computational Phonology (LASC11118)
Course Outline
| School | School of Philosophy, Psychology and Language Sciences |
College | College of Humanities and Social Science |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | In this course, we will consider how a computational perspective can shed light on problems in phonological analysis, such as the learning of allophonic rules, phonotactic constraints, and syllabification. The course will provide an introduction to finite-state and probabilistic methods, but the focus will be on the practical use of computational tools to explore a range of phonological problems, as well as on reading and critical discussion of primary literature. |
| Course description |
Assessment will consist of coursework (homeworks and tutorial exercises), weekly reading summaries, and a final project. Course time will be split between practical lab work, lectures, and discussions. Students will be responsible for leading discussion on at least one paper over the course of the semester.
Some prior programming experience (using any language) is recommended, but not strictly required.
Formative feedback available:
- students will give short presentations about their project proposal and will receive feedback thereafter
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Course Delivery Information
| Not being delivered |
Learning Outcomes
On completion of this course, the student will be able to:
- have an overview of the use of computational models in phonological analysis
- have an ability to read and assess literature in computational phonology and present the results to a peer audience
- have an understanding of the fundamentals of probability theory and finite-state methods
- have an ability to apply computational methods in (morpho)phonological analysis
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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. |
| Additional Class Delivery Information |
Attend all lectures as scheduled |
| Keywords | Not entered |
Contacts
| Course organiser | Dr James Kirby
Tel: (0131 6)50 3952
Email: jkirby1@exseed.ed.ac.uk |
Course secretary | Miss Toni Noble
Tel: (0131 6)51 3188
Email: Toni.noble@ed.ac.uk |
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