Coordinates  T/Th, 23:20pm, STAG 212 [Registrar] [Canvas] [Teams] 
Instructor 
Liang Huang (huanlian@)

TAs 
Ning Dai (dain@)

Office hours  Liang: T/Th: 3:253:50pm, KEC 2069. Ning: even weeks: F 45pm; odd weeks: M 45pm (i.e., the Friday/Monday before HWs are due); KEC Atrium. 
Prerequisites 

Textbooks (optional) 

Grading (tentative) 

Other Policies 

MOOCs (coursera) 

Objectives  This course provides an introduction to natural language processing, the study of human language from a computational perspective. We will cover finitestate machines (weighted FSAs and FSTs), syntactic structures (weighted contextfree grammars and parsing algorithms), and machine learning methods (maximum likelihood and expectationmaximization). 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, ForwardBackward, and InsideOutside). 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. 