Language and Information

3 credits · Graduate credit

This course introduces a body of quantitative techniques for modeling and analyzing natural language and for extracting useful information from texts. The theory includes Hidden Markov Models and the noisy channel model, information theory, supervised and unsupervised machine learning, and probabilistic context-free and context-sensitive grammars. Aspects of natural language analysis include phrasal lexicon induction, part of speech assignment, entity recognition, parsing, and statistical machine translation.