|Audience||PhD students in AI (and NLP in particular); MS students in AI who want to continue to PhD|
|Coordinates||TR, 4-5:20pm, BEXL 207 [Registrar] [Canvas]|
|Office hours||T 5:20-5:50pm and Th 3:30-3:55pm, KEC 2069 (Liang).|
M/F 2-3pm KEC Atrium (Juneki).
This course provides an introduction to natural language processing, the study of human language from a computational perspective.
We will cover finite-state machines (weighted FSAs and FSTs),
syntactic structures (weighted context-free grammars and parsing algorithms),
and machine learning methods (maximum likelihood and expectation-maximization). The focus will be on (a) modern quantitative techniques in NLP that use large corpora and statistical learning,
and (b) various dynamic programming algorithms (Viterbi, CKY, Forward-Backward, and Inside-Outside). At the end of this course, students should have a good understanding of the research questions and methods used in different areas of natural language processing. Students should also be able to use this knowledge to implement simple natural language processing algorithms and applications. Students should also be able to understand and evaluate original research papers in natural language processing that build on and go beyond the textbook material covered in class.