Fall 2019 - CMPT 413 D100

Computational Linguistics (3)

Class Number: 9002

Delivery Method: In Person


  • Course Times + Location:

    Sep 3 – Dec 2, 2019: Tue, 4:30–5:20 p.m.

    Sep 3 – Dec 2, 2019: Thu, 3:30–5:20 p.m.

  • Exam Times + Location:

    Dec 14, 2019
    Sat, 3:30–6:30 p.m.

  • Prerequisites:

    Completion of nine units in Computing Science upper division courses or, in exceptional cases, permission of the instructor.



This course examines the theoretical and applied problems of constructing and modelling systems, which aim to extract and represent the meaning of natural language sentences or of whole discourses, but drawing on contributions from the fields of linguistics, cognitive psychology, artificial intelligence and computing science.


This course is cross-listed with CMPT 825

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.


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



Final Project: 30%. 1 Midterm: 20%. 5 Homeworks: 45%. Participation 5%.

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).




Registrar Notes:

SFU’s Academic Integrity web site http://www.sfu.ca/students/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