Simon Fraser University
Engaging the World

Course Outlines

Fall 2022 - CMPT 413 D100

Computational Linguistics (3)

Class Number: 5252

Delivery Method: In Person

Overview

  • Course Times + Location:

    Sep 7 – Dec 6, 2022: Mon, 2:30–5:20 p.m.
    Burnaby

  • Prerequisites:

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

Description

CALENDAR DESCRIPTION:

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.

COURSE DETAILS:

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 instructing a robot to prepare your backpack for you. 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
  • Word representations
  • Supervised machine learning for NLP
  • Neural models for NLP
  • Sequence labeling
  • Machine translation
  • Parsing and semantics
  • NLP applications

Grading

NOTES:

Grading will be based on assignments, final project and class participation. Details will be announced during the first week of classes.

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

  • Speech and Language Processing (3rd ed), Dan Jurafsky and James H. Martin, 2019, https://web.stanford.edu/~jurafsky/slp3/
  • Neural Network Methods for Natural Language Processing, Yoav Goldberg, Morgan and Claypool, 2017, http://www.morganclaypool.com/doi/10.2200/S00762ED1V01Y201703HLT037
  • Natural Language Processing, Jacob Eisenstein, The MIT Press, 2018, https://github.com/jacobeisenstein/gt-nlp-class/blob/master/notes/eisenstein-nlp-notes.pdf

REQUIRED READING NOTES:

Your personalized Course Material list, including digital and physical textbooks, are available through the SFU Bookstore website by simply entering your Computing ID at: shop.sfu.ca/course-materials/my-personalized-course-materials.

Registrar Notes:

ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS

SFU’s Academic Integrity website 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