Natural Language Processing¶
Important note: This is a website hosting NLP-related teaching materials. If you are a student at NYU taking the course, please go to the official course website for up-to-date information.
We try to make all of the course material accessible. If you need additional accomadation, please send us an email.
How can we teach machines to understand language so that they can answer our queries, extract information from textual data, or even have a conversation with us? The primary goal of this course is to provide students with the principles and tools needed to solve a variety of NLP problems. We will focus on data-driven methods, including classification, sequence labeling, structured prediction, unsupervised learning, and deep learning. Specific applications include text classification, constituent parsing, semantic parsing, and generation.
Students are expected to have solid mathematic background and programming skills.
Probability, statistics, linear algebra (DS-GA.1002, MATH-UA.140, MATH-UA.235)
Algorithms and data structure (CSCI-UA.102)
Basic knowlege in machine learning (DS-GA.1003, CSCI-UA.0473) will be helpful
Textbook: There is no required textbook. Course notes/slides should be sufficient. Some lectures will be based on the following books (available freely online):
Dan Jurafsky and James H. Martin. Speech and Language Processing. A classic textbook covering both traditional and modern approaches to NLP.
Jacob Eisenstein. Introduction to Natural Language Processing. A comprehensive reference with additional coverage on relevant topics in linguistics and slightly more advanced topics in machine learning.
Yoav Goldberg. Neural Network Methods for Natural Language Processing. Covers neural network models for NLP.
Aston Zhang, Zack C. Lipton, Mu Li, and Alex J. Smola. Dive into Deep Learning. Covers many topics in neural networks and features numerous hands-on examples. We will use some examples from this book.
In the lecture notes, we will use JM, E, G, D2L to refer to the above books respectively.
Background: Here are some useful materials if you want to review the background knowledge.
Probability and optimization in the appendix of Eisenstein’s book.
Notes from DS-GA.1002.
Machine learning material from DS-GA.1003.
- Lecture notes