Postgraduate Course: Computational Phonology (LASC11118)
|School||School of Philosophy, Psychology and Language Sciences
||College||College of Humanities and Social Science
||Availability||Not available to visiting students
|Credit level (Normal year taken)||SCQF Level 11 (Postgraduate)
|Home subject area||Language Sciences
||Other subject area||None
||Taught in Gaelic?||No
|Course description||In 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
Entry Requirements (not applicable to Visiting Students)
||Other requirements|| None
|Additional Costs|| None
Course Delivery Information
|Delivery period: 2013/14 Semester 2, Not available to visiting students (SS1)
||Learn enabled: Yes
|Course Start Date
|Breakdown of Learning and Teaching activities (Further Info)
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
|Breakdown of Assessment Methods (Further Info)
|No Exam Information
Summary of Intended 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.
|Assessment will consist of three main components:|
1) Homeworks/tutorial exercises: These will be short (~500 words) and consist of: (a) a basic probability theory exercise; (b) a finite-state morpotactics exercise; (c) a Viterbi path exercise; (d) modifying, running, and comparing the results of various online learning algorithms; (e) building an n-gram model using the SRILM toolkit
2) Short (~500 words) weekly reading reports. In addition, each student will be responsible for leading discussion on a weekly research paper (possibly in pairs if enrolment is sufficiently high)
3) A final take-home project (~2,500-3,000 words). The precise details will vary with year, but possible projects include: proposal, implementation and assessment of a syllabification algorithm; training and assessing a simple neural network to learn inflectional class structure; or constructing a two-level morphophonology for a highly inflecting language
Final project deadline: Thursday 10th April 2014, 12 noon
Return deadline: Friday 2nd May 2014
||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.
||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.
|Course organiser||Dr James Kirby
Tel: (0131 6)50 3952
|Course secretary||Miss Toni Noble
Tel: (0131 6)51 3188
© Copyright 2013 The University of Edinburgh - 10 October 2013 4:40 am