MS HLT Required

Specialized work on an individual basis, consisting of training and practice in Human Language Technology in a academic, technical, business, or governmental establishment.

This course provides a hands-on project-based approach to particular problems and issues in computational linguistics.

Topics include speech synthesis, speech recognition, and other speech technologies. This course gives students background for a career in the speech technology industry. Graduate students will do extra readings, extra assignments, and have an extra presentation. Their final project must constitute original work in a speech technology.

This course introduces the key concepts underlying statistical natural language processing. Students will learn a variety of techniques for the computational modeling of natural language, including: n-gram models, smoothing, Hidden Markov models, Bayesian Inference, Expectation Maximization, Viterbi, Inside-Outside Algorithm for Probabilistic Context-Free Grammars, and higher-order language models. Graduate-level requirements include assignments of greater scope than undergraduate assignments.

Fundamentals of formal language theory; syntactic and semantic processing; the place of world knowledge in natural language processing. Graduate-level requirements include a greater number of assignments and a higher level of performance.

An introduction to syntactic theory with an emphasis on data analysis, critical thinking, and theory development. Taught within the generative Principles and Parameters approach to syntax. Graduate-level requirements include a greater number of problems.