His interests include probabilistic models of language and statistical natural language processing, constraint-based theories of grammar, computational lexicography, syntax, and information extraction. To get people to understand as much as possible about important ideas in NLP and recent work in NLP within one quarter. It develops an in-depth understanding of both the algorithms available for the processing of linguistic information and the underlying computational properties of natural languages. Word-level, syntactic, and semantic processing from both a linguistic and an algorithmic perspective are considered. Also, it examines and constructs representative systems.
Programs can be in any language appropriate to the task.
Basic familiarity with logic, vector spaces, and probability. Intended Audience Graduate students and advanced undergraduates specializing in computer science, linguistics, or symbolic systems. The required text is Christopher Manning and Hinrich Sch tze, Foundations of Statistical Natural Language Processing. MIT Press, 1999. As an additional optional text, you will also find in the bookstore Jurafsky and Martin, Speech and Language Processing.