Tag Archives: Areal
Field Methods

Keren Rice – University of Toronto
Course time: Monday/Tuesday/Wednesday/Thursday 3:30-5:20
2437 Mason Hall
Note: This class may count for double credit.

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This course is an introduction to linguistic field methods. We will work with a speaker of a language that none of us know, endeavoring to discover as much as possible about the structure of the language, at all levels – phonetic, phonological, morphological, syntactic, semantic – through a combination of structured questioning and working with texts that we will record from the speaker. The emphasis will be on how to discover the systematicity of an unknown language on its own terms.

Prerequisite: Background in linguistics. Students should be able to transcribe, do morphological analysis, and syntactic analysis.

Recommended co-requisite: Tools for Language Documentation (Claire Bowern)

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Gesture and Gestural Documentation

Mandana Seyfeddinipur – The School of Oriental and African Studies (SOAS)
Course time: Monday/Wednesday 11:00 am – 12:50 pm
2427 Mason Hall

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In the past ten years the study of hand gestures has become an established area of investigation in different disciplines. This course will provide an introduction to theoretical and methodological issues in manual gesture research. The course will provide a solid foundation for further research into the phenomenon by the course participants. We will explore the role of manual gesture in language, culture and cognition and provide hands on training in methods in gesture research. The basic functions of gesture in communication, its interaction with speech in the creation of meaning as well as its role in cognition will be introduced. One focus will be how to document gesture in actual language use doing fieldwork.  In the practical component participants will learn how to record gesture data in naturalistic as well as in experimental settings. In addition the course will provide the opportunity to learn how to annotate and code gesture with available software. Participants are encouraged to bring their own recordings for annotation and analyses. Some familiarity with general linguistics is presumed.

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Machine Learning

Steve Abney – University of Michigan
Course time: Monday/Wednesday 11:00 am – 12:50 pm
1401 Mason Hall

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This course provides a general introduction to machine learning. Unlike results in learnability, which are very abstract and have limited practical consequences, machine learning methods are eminently practical, and provide detailed understanding of the space of possibilities for human language learning.

Machine learning has come to dominate the field of computational linguistics: virtually every problem of language processing is treated as a learning problem.  Machine learning is also making inroads into mainstream linguistics, particularly in the area of phonology. Stochastic Optimality Theory and the use of maximum entropy models for phonotactics may be cited as two examples.

The course will focus on giving a general understanding of how machine learning methods work, in a way that is accessible to linguistics students. There will be some discussion of software, but the focus will be on understanding what the software is doing, not in the details of using a particular package.

The topics to be touched on include classification methods (Naive Bayes, the perceptron, support vector machines, boosting, decision trees, maximum entropy classifiers) and clustering (hierarchical clustering, k-means clustering, the EM algorithm, latent semantic indexing), sequential models (Hidden Markov Models, conditional random fields) and grammatical inference (probabilistic context-free grammars, distributional learning), semisupervised learning (self-training, co-training, spectral methods) and reinforcement learning.

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