Simon Fraser University
Engaging the World

Course Outlines

Fall 2017 - CMPT 825 G100

Natural Language Processing (3)

Class Number: 8272

Delivery Method: In Person

Overview

  • Course Times + Location:

    Sep 5 – Dec 4, 2017: Fri, 1:30–4:20 p.m.
    Burnaby

Description

CALENDAR DESCRIPTION:

In this course, theoretical and applied issues related to the development of natural language processing systems and specific applications are examined. Investigations into parsing issues, different computational linguistic formalisms, natural language syntax, semantics, and discourse related phenomena will be considered and an actual natural language processor will be developed.

COURSE DETAILS:

This course is cross-listed with CMPT 413

Imagine a world where you can pick up a phone and talk in English, while at the other end of the line your words are spoken in Chinese. Imagine a computer animated representation of yourself speaking fluently what you have written in an email. Imagine automatically uncovering protein/drug interactions in petabytes of medical abstracts. Imagine feeding a computer an ancient script that no living person can read, then listening as the computer reads aloud in this dead language. Natural Language Processing is the automatic analysis of human languages such as English, Korean, and thousands of others analyzed by computer algorithms that can make these applications possible. Unlike artificially created programming languages where the structure and meaning of programs is easy to encode, human languages provide an interesting challenge, both in terms of its analysis and the learning of language from observations.

Topics

  • Language models
  • Edit distance
  • Supervised machine learning for NLP
  • Sequence labeling
  • Unsupervised machine learning for NLP
  • Machine translation
  • Parsing and semantics

Grading

NOTES:

Final Project: 30%. 1 Midterm: 18%. 5 Homeworks: 52%.

Students must attain an overall passing grade on the weighted average of exams in the course in order to obtain a clear pass (C- or better).

Materials

MATERIALS + SUPPLIES:

Reference Books

  • Foundations of Statistical Natural Language Processing, Christopher Manning and Hinrich Schutze, MIT Press, 1999, 9780262133609

RECOMMENDED READING:

Speech and Language Processing 2nd Edition, Daniel Jurafsky and James Martin, Prentice-Hall, 2008, 
See: http://www.cs.colorado.edu/~martin/slp2.html
ISBN: 9780131873216

Natural Language Processing with Python, Steven Bird, Ewan Klein, and Edward Loper,
O'Reilly Media, 2009,
See: http://www.nltk.org/book - Can be downloaded
ISBN: 9780596516499

Graduate Studies Notes:

Important dates and deadlines for graduate students are found here: http://www.sfu.ca/dean-gradstudies/current/important_dates/guidelines.html. The deadline to drop a course with a 100% refund is the end of week 2. The deadline to drop with no notation on your transcript is the end of week 3.

Registrar Notes:

SFU’s Academic Integrity web site http://students.sfu.ca/academicintegrity.html is filled with information on what is meant by academic dishonesty, where you can find resources to help with your studies and the consequences of cheating.  Check out the site for more information and videos that help explain the issues in plain English.

Each student is responsible for his or her conduct as it affects the University community.  Academic dishonesty, in whatever form, is ultimately destructive of the values of the University. Furthermore, it is unfair and discouraging to the majority of students who pursue their studies honestly. Scholarly integrity is required of all members of the University. http://www.sfu.ca/policies/gazette/student/s10-01.html

ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS