Computation Linguistics - PHD

Students are introduced to computer programming as it pertains to collecting and analyzing linguistic data. The particular programming language is chosen at the discretion of the instructor. Graduate-level requirements include more challenging exams; 50% greater contribution to their respective group projects; 9 instead of 6 assignment; additional readings from the primary literature.

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.