Tag Archives: Computational/Corpus
7/14 How the Brain Accommodates Variability in Linguistic Representations

July 14, 2013
Aud C, Angell Hall

Organizer Contact: T. Florian Jaeger (fjaeger@bcs.rochester.edu)

Click here for workshop website.

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Computational Modeling of Sound Change

James Kirby – University of Edinburgh
Morgan Sonderegger – McGill University
Course time: Tuesday/Thursday 3:30-5:20 pm
2347 Mason Hall

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Decades of empirical research have led to an increasingly nuanced picture of the nature of phonetic and phonological change, incorporating insights from speech production and perception, cognitive biases, and social factors. However, there remains a significant gap between observed patterns and proposed mechanisms, in part due to the difficulty of conducting the type of controlled studies necessary to test hypotheses about historical change. Computational and mathematical models provide an alternative means by which such hypotheses can be fruitfully explored. With an eye towards Box’s dictum (all models are wrong, but some are useful), this course asks: how can computational models be useful for understanding why phonetic and phonological change occurs?  Students will study the growing and varied literature on computational and mathematical modeling of sound change that has emerged over the past decade and a half, including models of phonetic change in individuals over the lifespan, phonological change in speech communities in historical time, and lexical diffusion. Discussion topics will include the strengths and weaknesses of different approaches (e.g.simulation-based vs. mathematical models); identifying which modeling frameworks are best suited for particular types of research questions; and methodological considerations in modeling phonetic and phonological change. For this course, some background in probability theory, single-variable calculus, and/or linear algebra is helpful but not required.

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Quantitative and Computational Phonology

Bruce Hayes – University of California, Los Angeles
Course time: Tuesday/Thursday 9:00-10:50 am
2306 Mason Hall

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In the grammar architecture of classical Optimality Theory (Prince and Smolensky 1993), constraints are ranked and the grammar generates exactly one winner per input. Phonologists have proposed instead that we should consider models in which the constraints, rather than being ranked, bear weights (real numbers, intuitively related to constraint strength). Weights are employed to calculate probabilities for all members of the candidate set.

Such quantitative grammars open up new research possibilities for constraint-based phonology:

(a) Modeling free variation and the multiple factors that shift the statistical distribution of outputs across contexts;

(b) Modeling gradient intuitions (intermediate well-formedness, ambivalence among output choices);

(c) Modeling quantitative lexical patterns and how they are characteristically mimicked in experiments where native speakers are tested on their phonological knowledge;

(d) Modeling phonological learning:  even where in areas where the ambient language doesn’t vary at all, the child’s conception of what is likely to be the correct grammar of it will change (approaching certainty) as more data are taken in; modeling can trace this process.

This course will be an introduction to these models and research areas. It will emphasize learning by doing. Participants will use software tools that embody the theories at hand and will examine and model data from a variety of digital corpora. The course will not cover computational phonology per se, but it will cover enough computation to give participants a good understanding of the tools they are using. Pre-requisite for this course: a course in phonology.

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Structure and Evolution of the Lexicon

Janet Pierrehumbert – Northwestern University
Course time: Tuesday/Thursday 11:00 am – 12:50 pm
2353 Mason Hall

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This class will explore the basic principles that create and sustain the richness of the lexicon in human languages. We will consider how new words are created, how they are learned, and how they are replicated through social interactions in human communities. Empirical data will be drawn from classical sources, from language on the Internet, and from computer-based “games with a purpose”. Using concepts from research on population biology and social dynamics, we will also discuss mathematical approaches to modeling the life and death of words.

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