CS546 gives a graduate-level introduction to the statistical and machine learning methods used in natural language processing. We will largely focus on neural approaches this year, but may also cover other kinds of approaches. Prerequisites are a basic understanding of NLP, probability, statistics, linear algebra and machine learning, as well as solid programming skills. Students will learn to read the current literature, and apply these models to NLP problems. They will be required to do a research project, to give class presentations, and to write critical reviews of relevant papers.

Required textbook

Goldberg (2017) Neural Network Methods for Natural Language processing (you can get the PDF for free through the University)


35% paper presentation
50% research project
10% paper reviews
5% class participation


01/16 Introduction Overview, Policies pdf
01/18 Motivation What is NLP? Why neural models for NLP? pdf
01/23 Neural network basics pdf
01/25 Neural network basics pdf