Natural Language Processing

11-411 for undergrads | 11-611 for grads

Course Description

This course is about a variety of ways to represent human languages (like English and Chinese) as computational systems, and how to exploit those representations to write programs that do neat stuff with text and speech data, like
  • translation,
  • summarization,
  • extracting information,
  • question answering,
  • natural interfaces to databases, and
  • conversational agents.

This field is called Natural Language Processing or Computational Linguistics, and it is extremely multidisciplinary. This course will therefore include some ideas central to Machine Learning and to Linguistics.

We'll cover computational treatments of words, sounds, sentences, meanings, and conversations. We'll see how probabilities and real-world text data can help. We'll see how different levels interact in state-of-the-art approaches to applications like translation and information extraction.

From a software engineering perspective, there will be an emphasis on rapid prototyping, a useful skill in many other areas of Computer Science.

Course Prerequisites

CS courses on data structures and algorithms, and strong programming skills.


Date Topic Readings Assignments and
Project milestones
1 Jan 15 Course overview; What does it mean to know language?
Lecture Video
Chap 1
2 Jan 17 Information extraction, question answering, and NLP in IR
Lecture Video
Chap 22.0-2, 23.0-2
3 Jan 22 Project
4 Jan 24 Words, morphology, and lexicons Chap 3.1-3.9 Assignment 1 out
5 Jan 29 Language models and smoothing Chap 4.3-8
6 Jan 31 Noisy channel models and edit distance Chap 3.10, 3.11, 5.9 Assignment 1 due
Assignment 2 out
7 Feb 5 Classification
8 Feb 7 Part of speech tags Chap 5.0-3 Assignment 2 due
Assignment 3 out
9 Feb 12 Hidden Markov models Chap 6.0-4
10 Feb 14 Syntactic representations of natural language Chap 12.0-3 Assignment 3 due
Assignment 4 out
11 Feb 19 Chomsky hierarchy and natural language Chap 15
12 Feb 21 Context-free recognition, CKY
13 Feb 26 Parsing algorithms Chap 13
14 Feb 28 Parsing algorithms contd. Chap 12.7, Chap 14-14.2 Assignment 4 due
Assignment 5 out
Project Progress Report due 11:59pm
15 Mar 5 Treebanks and PCFGs
Chap 12.4, 14.7
Mar 7 Midterm exam
16 Mar 19 Lexical semantics
Chap 17.0-2, 19.0-3
17 Mar 21 Word embeddings/vector semantics
SLP3 Chap 6 Assignment 5 due
Assignment 6 out
18 Mar 26 Verb/sentence semantics
Chap 17.2-4, Chap 19.4-6
19 Mar 28 Compositional semantics, semantic parsing
Chap 18.1-3
20 Apr 2 Word Sense Disambiguation and Semantic Role Labelling
Chap 21
21 Apr 4 Discourse, entity linking, pragmatics
Chap 20.0-6, 20.8-11 Assignment 6 due
Assignment 7 out
22 Apr 9 Speech 1
Project dry run code due 11:59 PM
23 Apr 16 Speech 2
24 Apr 18 Non-English NLP
Assignment 7 due
25 Apr 23 Interpreting Social Media
26 Apr 25 Machine Translation
Chap 25.0-1, 25.9 Final Project code due 11:59 PM
27 Apr 30 Deep Learning
28 May 2 Conclusion
Final Project report due 11:59pm
TBD Final exam

Competitive Project

A major component will be the project: build a program whose input is a web page P and whose output is a set of questions about the content in P (that a human could answer if she read P), and can also, if given a question Q about the content of P, answer the question intelligently. Projects will be pitted against each other in a competition at the end of the course.


Students will be evaluated by exam (midterm and final, totaling 40%), regular short quizzes and weekly pencil-and-paper or small programming homework problems (30% together), and the group project (30%).


Should I take this course?

Yes, if:

  • you're a CS student interested in languages, language technology, or information processing
  • you're a CS student who needs an "applications" credit
  • you're a language technology minor (this course is an elective option)
  • you're a linguistics student who can write computer programs (this course is an elective option)
  • you always suspected natural language was kind of like Lisp (or Java or ...)
  • you want computers to take over the world
  • you don't want computers to take over the world, but if they do, you want to negotiate your release
  • you like AI, machine learning, and/or theoretical computer science, and want to apply them to a hard real-world problem

Related courses elsewhere (not exhaustive!)

University of California, Berkeley, Brown University, University of Colorado, Columbia University, Cornell University, University of Illinois at Urbana-Champaign, Johns Hopkins University, University of Maryland, New York University, University of Pennsylvania, Stanford University, University of Utah, University of Wisconsin-Madison