Computer Science 4750:
Introduction to Natural Language Processing
(Fall 2023)

http://www.cs.mun.ca/~harold/Courses/CS4750

Instructor	Office	Office Hours	Lectures
Todd Wareham harold@mun.ca	EN-2034 (709) 864-4601	TBA	TR, 9:00-10:15 am Room EN-1051

Lecture Outline (Tentative)

• Background: Linguistics and Natural Language Processing (2 weeks)
  
• Mechanisms for Natural Language Processing (1 1/2 weeks)
  
• Implementing NLP Mechanisms (Finite-state Automata and Transducers, Context-free Grammars, Hidden Markov Models, Neural Networks) (4 weeks)
  
• Applications (e.g., Information Extraction, Spelling Correction, Machine Translation) (2 weeks)
  
• Student presentations (2 weeks)

The lectures and displays (and all material) delivered or provided in this course by the instructor, including any visual or audio recording thereof, are subject to copyright owned by the instructor.. It is prohibited to record or copy by any means, in any format, openly or surreptitiously, in whole or in part, in the absence of express written permission from the instructor any of the lectures or materials provided or published in any form during or from the course.

Evaluation Scheme

• There will be no supplementary assignments. Extensions for missed assignments (with appropriate documentation) will be given at the instructor's discretion. Where such extensions are not given, either (1) late assignments will be docked 25% or (2) marks for missed assignments (with appropriate documentation) will be averaged over the remaining assignments at the instructor's discretion.
• There will be no supplementary exams. Marks for missed in-class exams (with appropriate documentation) will be averaged over the remaining exams at the instructor's discretion.

Assignment Deadlines (Tentative)

Recommended Readings

• Bird, S., Klein, E., and Loper, E. (2009) Natural Language Processing with Python. O'Reilly Media.
• Eisenstein, J. (2019) Introduction to Natural Language Processing. The MIT Press.
• Indurkhya, N. and Damerau, F.J. (eds.) (2010) Handbook of Natural Language Processing (2nd Edition). Chapman and Hall / CRC.
• Jurafsky, D. and Martin, J.H. (2008) Speech and Natural Language Processing (2nd Edition). Prentice-Hall.
• Jurafsky, D. and Martin, J.H. (2022) Speech and Natural Language Processing (3rd Edition). (Book Website)
• Kedia, A, and Rasu, M. (2020) Hands-On Python Natural Language Processing. Packt Publishing; Birmingham, UK.
• Mitkov, R. (ed.) (2005) The Oxford Handbook of Computational Linguistics. Oxford University Press.
• Rao, D. and McMahan, B. (2019) Natural Language Processing with PyTorch: Build Intelligent Language Applications Using Deep Learning. O'Reilly; Boston.
• Roche, E. and Schabes, Y. (eds.) (1997) Finite-state Natural Language Processing. The MIT Press.
• Vajjala, S., Majumder, B., Gupta, A., and Surana, H. (2019) Practical Natural Language Processing: A Comprehensive Guide to Building Real-world NLP Systems. O'Reilly; Boston, MA.

• Memorial University of Newfoundland is committed to supporting inclusive education based on the principles of equity, accessibility, and collaboration. Accommodations are provided within the scope of the University Policies for the Accommodations of Students with Disabilities ( www.mun.ca/policy/site/policy.php?id=239). Students who may need an academic accommodation are asked to initiate the request with the Glenn Roy Blundon Centre at the earliest opportunity ( https://www.mun.ca/student/about-us/units-and-contacts/accessibility-services---the-blundon-centre/).
• Students are expected to adhere to those principles which constitute proper academic conduct. A student has the responsibility to know which actions, as described under Academic Offences in the University Regulations, could be construed as dishonest and improper. Students found guilty of an academic offence may be subject to a number of penalties commensurate with the offence including reprimand, reduction of grade, probation, suspension or expulsion from the University. For more information regarding this policy, students should refer to the University Regulations for Academic Misconduct (Section 6.12) in the University Calendar.

Created: July 1, 2023
Last Modified: September 11, 2023