|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)|
|Instructors||T. Florian Jaeger (firstname.lastname@example.org) and Roger Levy (email@example.com)|
|Instructor office||University Club 216|
|Office hours||Tuesday 12:30-1:30pm (Jaeger); Monday 5:15-6:15pm (Levy)|
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.
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.
There is a mailing list for the class; please sign up for it here!
|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|
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: