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 16 Course overview; What does it mean to know language?
Chap 1
2 Jan 18 Information extraction, question answering, and NLP
in IR
Chap 22.0-2, 23.0-2
3 Jan 23 Project
Slides Example video
4 Jan 25 Words, morphology, and lexicons
Chap 3.1-3.9
5 Jan 30 Language models and smoothing
Chap 4.3-8
6 Feb 1 Noisy channel models and edit distance
Chap 3.10, 3.11, 5.9 Assignment 1 due
7 Feb 6 Classification
8 Feb 8 Part of speech tags
Chap 5.0-3 Assignment 2 due
9 Feb 13 Hidden Markov models
Chap 6.0-4
10 Feb 15 Syntactic representations of natural language
Chap 12.0-3 Assignment 3 due
11 Feb 20 Chomsky hierarchy and natural language
Chap 15
12 Feb 22 Context-free recognition, CKY
Assignment 4 due
13 Feb 27 Parsing algorithms
Chap 13 Preliminary Project Report due
14 Mar 1 Parsing algorithms contd.
Chap 12.7, Chap 14-14.2 Assignment 5 due
15 Mar 6 Treebanks and PCFGs
Chap 12.4, 14.7
Mar 8 Midterm exam
Practice Problems
Practice Solutions
Mar 12-16 Spring Break
Mar 18
16 Mar 20 Lexical semantics
Chap 17.0-2, 19.0-3
17 Mar 22 Word embeddings/vector semantics
JM v3 Chap 15 and Chap 16 Project Progress Report due
18 Mar 27 Verb/sentence semantics
Slides A
Slides B
Chap 17.2-4, Chap 19.4-6
19 Mar 29 Compositional semantics, semantic parsing
Chap 18.1-3 Assignment 6 due
20 Apr 3 Discourse, entity linking, pragmatics
Chap 21
21 Apr 5 Word Sense Disambiguation and Semantic Role Labelling
Chap 20.0-6, 20.8-11 Assignment 7 due
22 Apr 10 Speech 1
23 Apr 12 Speech 2
Project dry run code due
24 Apr 17 Interpreting Social Media
Apr 19 No class
25 Apr 24 Deep Learning
Final Project code due
26 Apr 26 Machine Translation
Chap 25.0-1, 25.9
27 May 1 Non-English NLP
28 May 3 Conclusion
Final Project report due
TBA 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