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CSE 40657/60657
Natural Language Processing

Term
Fall 2019
Time
MWF 11:30am–12:20pm
Room
DeBartolo 310
Instructor
David Chiang

Computers process massive amounts of information every day in the form of human language. Although they do not understand it, they can learn how to do things like answer questions about it, or translate it into other languages. This course is a systematic introduction to the ideas that form the foundation of current language technologies and research into future language technologies.

The official prerequisites are CSE 20312 or CDT 30020. Students should be experienced with writing substantial programs in Python. The course also makes use of finite automata, context-free grammars, basic linear algebra, multivariable differential calculus, and probability theory. Ideally, students should have taken CSE 30151, Math 10560, and ACMS 30440, but please contact the instructor if you have questions about the necessary background.

Staff

Instructor
Prof. David Chiang
Office hours: MTh 1–2pm, 326D Cushing
Teaching assistant
Darcey Riley
Office hours: M 4–5pm, Th 2–3pm, 212 Cushing
Recitations: Mon 7pm (when announced), B011 DeBartolo

Announcements will be made on Campuswire, and the best way to contact the teaching staff is also on Campuswire.

Schedule

The readings for each week should be done before the lectures that week; homeworks and project milestones should be done by Friday at 5pm in that week.

Unit Week Assignment Mon Wed Fri
1 Chapter 1, Chapter 2
08/28
Language
08/30
Probability
Getting Language 2 Chapter 3 (v2), Chapter 4 (v3)
Project idea
09/02
Language models
09/04
Smoothing
09/06
Finite automata
3 Chapter 5 (v2), Chapter 6 (v3)
09/09
Recurrent neural networks
09/11
Text input
Finite transducers and composition
09/13
Viterbi algorithm
4 HW1: Text input
09/16
Text normalization
Unsupervised training
09/18
continued
09/20
continued
5 Chapter 7
Chapter 8
09/23
Phonetics and phonology
09/25
Speech recognition
09/27
Character and handwriting recognition
Analyzing Language 6 Chapter 9, Chapter 10
HW2: Text correction
09/30
Syntax
10/02
Morphology
10/04
Part-of-speech tagging
7 Chapter 11, Chapter 12
10/07
Context-free grammars
10/09
CKY algorithm
10/11
continued
8 Chapter 13
Project baseline
10/14
Adding annotations
10/16
Unsupervised annotation
10/18
Beyond CFGs
Fall break
Understanding Language 9 Chapter 14
10/28
Text classification
Bag of words
10/30
Topic modeling
Word embeddings
11/01
Projects
10 Chapter 15
HW3: Parsing
11/04
Entity recognition
11/06
Graph semantics and graph grammars
11/08
Projects
11 Slides
11/11
Logical semantics
11/13
continued
11/15
Projects
Generating Language 12 HW4: Entity recognition
11/18
Generation
11/20
continued
11/22
Projects
13
11/25
Word-based machine translation
Thanksgiving
14
12/02
Syntax-based machine translation
Synchronous CFGs
12/04
continued
12/06
Projects
15 HW5: Machine translation
12/09
Adding a language model
12/11
Conclusion
Final Project report

Requirements

Your work in this course consists of five homework assignments, a research project, and participation (whether you come to class, whether you appear to be paying attention, and whether you appear to have done the readings).

All written work should be submitted through Sakai.

requirement points
homeworks 5 × 30
project 4 × 30
participation 30
total 300
letter gradepoints
A 280–300
A− 270–279
B+ 260–269
B 250–259
B− 240–249
C+ 230–239
C 220–229
C− 210–219
D 180–209
F 0–179

Policies

Honor Code

Students in this course are expected to abide by the Academic Code of Honor Pledge: “As a member of the Notre Dame community, I will not participate in or tolerate academic dishonesty.”

The following table summarizes how you may work with other students and use print/online sources:

Resources Solutions
Consulting allowed not allowed
Copying cite not allowed
See the CSE Guide to the Honor Code for definitions of the above terms.

If an instructor sees behavior that is, in his judgement, academically dishonest, he is required to file either an Honor Code Violation Report or a formal report to the College of Engineering Honesty Committee.

Late Submissions

In the case of a serious illness or other excused absence, as defined by university policies, coursework submissions will be accepted late by the same number of days as the excused absence. Otherwise, you may submit part of an assignment on time for full credit and part of the assignment late with a penalty of 30% per week (that is, your score for that part will be $\lfloor 0.7^t s\rfloor$, where $s$ is your raw score and $t$ is the possibly fractional number of weeks late). No part of the assigment may be submitted more than once. No work may be submitted after the final project due date.

Students with Disabilities

Any student who has a documented disability and is registered with Disability Services should speak with the professor as soon as possible regarding accommodations. Students who are not registered should contact the Office of Disability Services.