University of Toronto
Computer Science 2528, Fall 2017
Advanced Computational Linguistics
Instructor: Graeme Hirst
CSC 2528 is a participatory course. The class meets once a week for
discussions of recent research papers in computational linguistics and natural
language processing. We will cover four topics this term, spending two or
three weeks on each. In addition, a couple of sessions will be devoted to the
topics of students’ term papers.
The topics for Fall 2017 will be
- CL in a post-truth world
- CL and psychology
- CL and political texts
issues in CL and NLP
Meetings, Fall 2017: Thursdays, 13:00 to 15:00, BA 2179, beginning
Thursday 7 September. The date of Reading Week is TBD.
Credit for the course will be based on in-class presentations and a
Course information sheet (PDF). For further
information, contact Graeme Hirst at (The initials of his name) @cs.toronto.edu
Topics, readings, and presenters
- Organization of the course.
“Who decides what a text means?”
CL in a post-truth world
14 September: Background; fake news
21 September: Rumours
- Background: Leon Derczynski, Kalina Bontcheva, Maria Liakata, Rob Procter, Geraldine Wong Sak Hoi, and Arkaitz Zubiaga (2017). SemEval-2017 Task 8: RumourEval: Determining rumour veracity and support for rumours. Proceedings of the 11th International Workshop on Semantic Evaluations (SemEval-2017), Vancouver, 69–76.
- Arkaitz Zubiaga, Maria Liakata, Rob Procter, Geraldine Wong Sak Hoi, and Peter Tolmie (2016). Analysing how people orient to and spread rumours in social media by looking at conversational threads. PLoS ONE 11(3): e0150989.
- Bonus paper: Ahmet Aker, Leon Derczynski, and Kalina Bontcheva (2017). Simple open stance classification for rumour analysis. arXiv:1708.05286v1.
28 September: Detection of deception and other kinds of fakes
CL and psychology
- TBA. While you're waiting, enjoy this.
12 or 19 October
19 or 26 October
CL and political texts
26 October or 9 November: Ideology recognition.
- Graeme Hirst, Yaroslav Riabinin, Jory Graham, Magali Boizot-Roche, and Colin Morris (2014). Text to ideology or text to party status? In: From Text to Political Positions: Text analysis across disciplines, edited by Bertie Kaal, Isa Maks, and Annemarie van Elfrinkhof, John Benjamins Publishing Company, pages 93–115.
Rachael Tatman, Amandalynne Paullada, Leo G. Stewart, and Emma S. Spiro (2017). Non-lexical features encode political affiliation on Twitter. Proceedings of the Second Workshop on Natural Language Processing and Computational Social Science, Vancouver, pages 63–67.
Daniel Preoțiuc-Pietro, Ye Liu, Daniel J. Hopkins, and Lyle Ungar (2017). Beyond binary labels: Political ideology prediction of Twitter users. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, Vancouver, pages 729–740.
2 November: Guest speaker: Ludovic Rheault
Ethical issues in CL and NLP
16 November: Intro to ethics and issues in NLP; built-in bias in NLP systems.
23 November: More issues.
If the meeting is not needed for student term-paper presentations.
The people’s choice
Presentations by students in the class based on their readings for their
Last modified, 22 August 2017. Comments and
corrections to Graeme Hirst at (The initials of his name) @cs.toronto.edu.