Computational Psycholinguistics: LSA 2011 (LING 7800)

Course information

Lecture dates Tuesdays & Fridays, 8 July-2 August 2011
Lecture Location Muenzinger Hall room 0046 (this is on the lowest basement floor of Muenzinger Hall)
Lecture Times 10:30am-12:15pm

Instructor information

Instructors T. Florian Jaeger (fjaeger@bcs.rochester.edu) and Roger Levy (rlevy@ucsd.edu)
Instructor office University Club 216
Office hours Tuesday 12:30-1:30pm (Jaeger); Monday 5:15-6:15pm (Levy)

Course Description

Over the last two decades, cognitive science has undergone a paradigm shift towards probabilistic models of the brain and cognition. Many aspects of human cognition are now understood in terms of rational use of available information in the light of uncertainty (e.g. models in memory, categorization, generalization and concept learning, visual inference, motor planning). Building on a long traditional of computational models for language, such rational models have also been proposed for language processing and acquisition. This class provides an overview to the newly emerging field of computational psycholinguistics, which combines insights and methods from linguistic theory, natural language processing, machine learning, psycholinguistics, and cognitive science into the study of how we understand and produce language. There has been a surge in work in this area, which is attracting scholars from many disciplines. The goal of this class is to provide students with enough background to start their own research in computational psycholinguistics.

Intended Audience

Graduate students and researchers in linguistics, cognitive science, psychology, computer science, and any other discipline who are interested in using computational modeling techniques, especially probabilistic modeling, to study human language processing.

Mailing List

There is a mailing list for the class; please sign up for it here!

Syllabus

Day Topic Slides Required core reading Required technical reading Supplemental reading
8 July Course introduction; elementary probability theory; incremental parsing Lecture 1 PPTX PDF Jurafsky, 1996, sections 2.1, 2.2, 4 (24pp) Jurafsky & Martin Ch. 12, sections 12.1-12.6 Yngve, 1960
12 July Surprisal Lecture 2 PPTX PDF Probabilistic Earley Parsing slides Hale, 2001 (8pp); Levy ms., Section 3 (8 pp) Jurafsky & Martin Ch. 13, sections 13.1-13.2; Ch. 14,sections 14.1, 14.10, and for the brave of heart section 13.4.2 Demberg & Keller, 2008, 2009; Hale, 2003, 2006; Levy, 2008 (Cognition); Smith & Levy, 2008
15 July Information theory, evolution, and the mental lexicon Lecture 3 Plotkin & Novak, 2000 (13pp); Pinker, 2000 (2pp); Piantadosi et al., 2011 (3pp); YouTube video on Claude Shannon Jurafsky & Martin Chapter 4, sections 4.1-4.3, 4.10-4.11 Manin, 2006; Nowak, Komarova, & Niyogi, 2002; Jaeger & Tily, 2011
19 July Communicatively efficient language production Lecture 4 Jaeger, 2010 (20+ pp); Aylett & Turk 2006 (11pp) Genzel & Charniak, 2002; Levy & Jaeger, 2007; Moscoso del Prado Martin, submitted
22 July The Ideal Speaker Lecture 5 Lindblom, 1990 (38pp) Ferreira, 2008; Bard & Aylett, 2005; Galati & Brennan, 2010
25 July Input uncertainty and noisy-channel Bayesian inference in word recognition & sentence comprehension Lecture 6 PPTX PDF; Bayesian Reader slides; Intersection of WFSA and WCFG| Levy et al., 2009 (5pp) Jurafsky & Martin Ch. 2, section 2.2 Connine et al., 1991; Feldman, Griffiths, & Morgan, 2009; Levy, 2008 (EMNLP); Levy, 2011; Norris, 2006, 2009; Norris & McQueen, 2008
29 July Adaptation Lecture 7 Kleinschmidt & Jaeger, 2011 (8pp); Kraljic, Samuel, & Brennan, 2008 (8pp) Kording, Tenenbaum, & Shadmehr, 2007 (8pp) Clayards et al., 2008; Farmer, Fine, & Jaeger, 2011; Pardo & Remez, 2006
2 Aug Language acquisition Lecture 8 Goldwater, Griffiths, and Johnson 2009, pages 1-12 (Sections 1-3; and read Appendix A.1 if you want the technical details) TBD

Course requirements

Your course grade will be based on the following requirements:

1. one short homework assignment to be handed out in class 1, due by the beginning of class 3. We may add a second short homework assignment later on in the course.

2. a collaborative group final project. A project can be: