Spring 2025 - CMPT 413 D100

Computational Linguistics (3)

Class Number: 5456

Delivery Method: In Person

Overview

Course Times + Location:

Jan 6 – Apr 9, 2025: Mon, Wed, 3:30–4:50 p.m.
Burnaby

Instructor:
Angel Chang
angelx@sfu.ca

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.

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.

This course is an introduction to NLP and will cover algorithms and techniques for processing text (using probabilistic models and neural networks) as well as basic linguistic concepts. A strong background in math probability, linear algebra, calculus is necessary. Students must also be comfortable with programming and implementing algorithms (coding assignments will be in Python and Pytorch). In addition, familiarity with basic machine learning and deep learning concepts are highly recommended.

Topics

Language models
Word representations
Supervised machine learning for NLP
Neural models for NLP
Sequence labeling
Machine translation
Parsing and semantics
NLP applications

COURSE-LEVEL EDUCATIONAL GOALS:

At the conclusion of the course, the student is expected to gain an understanding of the algorithms and techniques used in NLP, including how computers represent words and the modeling of text as sequences, and the mathematics of machine learning models (e.g. language models, RNNs, transformers) commonly used in NLP and how they are trained. In addition, students will also gain knowledge about basic NLP tasks such as part-of-speech tagging and parsing, and how NLP models are used in applications such as machines translation.

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).

REQUIREMENTS:

There are no formal prerequisites for this class. However, you are expected to be familiar with the following:

Proficiency in Python - Programming assignments will be in python (numpy and pytorch will be used).
Calculus and Linear Algebra (MATH 151, MATH 232/240) - You will need to be comfortable with taking multivariable derivatives
Basic Probability and Statistics (STAT 270)
Basic Machine Learning (CMPT 410) is strongly recommended

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:

Speech and Language Processing (3rd ed)
Dan Jurafsky and James H. Martin, 2024,
https://web.stanford.edu/~jurafsky/slp3/

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

RELIGIOUS ACCOMMODATION

Students with a faith background who may need accommodations during the term are encouraged to assess their needs as soon as possible and review the Multifaith religious accommodations website. The page outlines ways they begin working toward an accommodation and ensure solutions can be reached in a timely fashion.

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Course Outlines