Introduction to Natural Language Processing
TA: Priya Sidhaye
This course presents an introduction to the computational modelling of natural language. Topics covered include: computational morphology, language modelling, syntactic parsing, lexical and compositional semantics, and discourse analysis. We will consider selected applications such as automatic summarization, machine translation, and speech processing. We will also study machine learning algorithms that are used in natural language processing.
Prerequisites: Knowledge of probabilities and statistics (e.g., MATH 323 or ECSE 305); algorithms (COMP 251 or COMP 252); programming experience.
Useful but not required: Background in artificial intelligence (e.g., COMP 424); introductory course in linguistics (LING 201).
Instructor permission is required to register. To obtain this permission, send me an e-mail stating how you meet the prerequisites listed above, along with your McGill ID, and whether you are an undergraduate or graduate student.
• Office hours on Oct 27 will be shortened to 2:30pm-2pm, and will take place in MC 103.
• There was a mistake in Assignment 1 Question 3 and in lecture4.pdf for the definition of Good-Turing smoothing. These have now been fixed.
• I'm holding a make-up office hours on Sept 23, 3pm-4pm in MC108N.
• Office hours cancelled on Sept 15 and 22. E-mail me to make an appointment if you need to meet!
• The Schulich Library is offering a workshop on Library Research Methods for Computer Science Topics. You are encouraged to attend!
Description: In this hands-on workshop, you will learn to: (1) efficiently use relevant library resources to search for a variety of research material on a computer science research topic, (2) manage the references that you gather throughout the research by using EndNote, a citation management program (freely available for McGill students), and (3) address common questions about writing and citing.
When: Thursday, October 1st, from 3:00 to 4:30 pm
Where: Schulich Library room 313
Lectures and Readings
Acknowledgements: Portions of the course slides are based on material from a similar course by Frank Rudzicz at the University of Toronto.
Assignment 2 - due on Oct 20 at the start of class (1pm)
Assignment 3 - due on Nov 17 at the start of class (1pm)
• Starter code and data for question 2
• Mitchell and Lapata, 2008
• Please submit the hard copy of the response to the paper separately from the rest of the assignment.
The midterm will be held in class on Tuesday, Nov 10. It will cover everything up to the end of week 9. The best way to study is to review the course slides, in-class exercises, and the assignments. Here is an additional list of optional exercises from the textbook.