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
• Ethical issues in CL and NLP

Meetings, Fall 2017: Thursdays, 13:00 to 15:00, BA 2179, beginning Thursday 7 September.

Credit for the course will be based on in-class presentations and a term paper.

Course information sheet (PDF). For further information, contact Graeme Hirst at (The initials of his name) @cs.toronto.edu

Topics, readings, and presenters

Introduction

7 September

• Organization of the course.
• Graeme: “Who decides what a text means?”

CL in a post-truth world

14 September: Background; fake news

• Background: Robert Dale (2017). NLP in a post-truth world. Natural Language Engineering 23(2): 319–324. [May require UofT IP address for access.]
• Misha: Victoria L. Rubin, Niall J. Conroy, Yimin Chen, and Sarah Cornwell (2016). Fake news or truth? Using satirical cues to detect potentially misleading news. Proceedings of the Second Workshop on Computational Approaches to Deception Detection, San Diego, 7–17.
• Chloé: Svitlana Volkova, Kyle Shaffer, Jin Yea Jang and Nathan Hodas (2017). Separating facts from fiction: Linguistic models to classify suspicious and trusted news posts on Twitter. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Short Papers), Vancouver, 647–653.

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.
• Sayyed: 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.
• Saša: 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

• Nona: Victoria L. Rubin and Tatiana Vashchilko (2012). Identification of truth and deception in text: Application of vector space model to rhetorical structure theory. Proceedings of the EACL 2012 Workshop on Computational Approaches to Deception Detection, Avignon, 97–106.
• Lebo: Jiwei Li, Myle Ott, Claire Cardie, and Eduard Hovy (2014). Towards a general rule for identifying deceptive opinion spam. Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, Baltimore, 1566–1576.
• Toryn: Dirk Hovy (2016). The enemy in your own camp: How well can we detect statistically-generated fake reviews — An adversarial study. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, Berlin, 351–356.

CL and psychology

5 October: Introduction; Axis I disorders

• Background: Emiel Krahmer (2010). What computational linguists can learn from psychologists (and vice versa). Computational Linguistics, 36(2), June 2010, 285–294.
• Serena: Philip Resnik, William Armstrong, Leonardo Claudino, Thang Nguyen, Viet-An Nguyen, and Jordan Boyd-Graber (2015). Beyond LDA: Exploring supervised topic modeling for depression-related language in Twitter. Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality, Denver, 99–107.
• Eddie: David N. Milne, Glen Pink, Ben Hachey, and Rafael A. Calvo (2016). CLPsych 2016 shared task: Triaging content in online peer-support forums. Proceedings of the 3rd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality, San Diego, 118–127.
• Guest: Judy Hanwen Shen: Judy Hanwen Shen and Frank Rudzicz (2017). Detecting anxiety on Reddit. Proceedings of the Fourth Workshop on Computational Linguistics and Clinical Psychology, Vancouver, 58–65.

12 October: Neurological disorders

• Pashootan: Kathleen C. Fraser, Jed A. Meltzer, and Frank Rudzicz (2016). Linguistic features identify Alzheimer’s disease in narrative speech. Journal of Alzheimer’s Disease 49, 407–422. [May require UofT IP address for access.]
• Josh: Michelle Lee, Gary E. Martin, Abigail Hogan, Deanna Hano, Peter C. Gordon, and Molly Losh (2017). What’s the story? A computational analysis of narrative competence in autism. Autism, online publication 17 January 2017. [May require UofT IP address for access.]

CL and political texts

19 October: Ideology recognition.

• Graeme: 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.
• Soukayna: 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.
• Mohamed: 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.

26 October: Beyond ideology recognition.

• Kawin: Viet-An Nguyen, Jordan Boyd-Graber, Philip Resnik, and Kristina Miler (2015). Tea Party in the House: A hierarchical ideal point topic model and its application to Republican legislators in the 112th Congress. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, Beijing, 1438–1448.
• Muuo: Stefano Menini, Federico Nanni, Simone Paolo Ponzetto, Sara Tonelli (2017). Topic-based agreement and disagreement in US electoral manifestos. Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, Copenhagen, 2928–2934.

2 November: Guest speaker: Ludovic Rheault

• Ludovic Rheault, Kaspar Beelen, Christopher Cochrane, and Graeme Hirst (2016). Measuring emotion in parliamentary debates with automated textual analysis. PLoS ONE 11(12): e0168843.

Ethical issues in CL and NLP

16 November: Intro to ethics and issues in NLP; built-in bias in NLP systems.

• Background: Dirk Hovy and Shannon L. Spruit (2016). The social impact of natural language processing. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, Berlin, pages 591–598.
• Talk by Graeme from keynote at First Workshop on Ethics in Natural Language Processing, Valencia, April 2017.
• Aditya: Aylin Caliskan, Joanna J. Bryson, and Arvind Narayanan (2017). Semantics derived automatically from language corpora contain human-like biases. Science 356 (6334), 183–186 (13 April 2017). [May require UofT IP address for access.]

The people’s choice

Presentations by students in the class, overviewing the topic of their term papers.

23 November

• Misha, Toryn, Pashootan, Soukayna

30 November

• Kawin, Saša, Lebo, Eddie