Spring 2023
Princeton University
This course introduces the basics of NLP, including recent deep learning approaches. It covers a wide range of topics, such as language modeling, text classification, machine translation, and question answering.
Recent advances have ushered in exciting developments in natural language processing (NLP), resulting in systems that can translate text, answer questions and even hold spoken conversations with us. This course will introduce students to the basics of NLP, covering standard frameworks for dealing with natural language as well as algorithms and techniques to solve various NLP problems, including recent deep learning approaches. Topics covered include language modeling, representation learning, text classification, sequence tagging, syntactic parsing, machine translation, question answering and others.
No data.
There is no required textbook for this class, and you should be able to learn everything from the lectures and assignments. However, if you would like to pursue more advanced topics or get another perspective on the same material, here are some books (all of them can be read free online):