Computational Psycholinguistics: ESSLLI 2009 Advanced Course in Language & Computation

Course information

Lecture dates July 20—24
Lecture Times 17:15-18:45pm
Class webpage

Instructor information

Instructor Roger Levy (
Instructor title Assistant Professor, Department of Linguistics, University of California at San Diego

Course Description

Over the last two decades, computational linguistics has been revolutionized by increases in computing power, large linguistic datasets, and a paradigm shift toward the view that language processing by computers is best approached through the tools of statistical inference. During roughly the same time frame, there have been similar theoretical developments in cognitive psychology towards a view of major aspects of human cognition as instances of rational statistical inference, exemplified by work such as Anderson (1990) and Tenenbaum & Griffiths (2001). Developments in these two fields have set the stage for renewed interest in computational approaches to human language processing. Correspondingly, this course covers some of the most exciting developments in computational psycholinguistics over the past decade. The course focuses on probabilistic knowledge and memory in language processing, covering models, algorithms, and key empirical results in the literature.

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.

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Day Topic Slides Core reading Supplemental reading
20 July Non-probabilistic, memory-focused models of incremental comprehension Lecture 1 Yngve, 1960 Miller & Chomsky, 1963; Abney & Johnson, 1991; Gibson, 1998, 2000; Morrill, 2000; Lewis & Vasishth, 2005
21 July Probabilistic grammars and human sentence comprehension as incremental probabilistic parsing Lecture 2 Narayanan & Jurafsky, 1998 Jurafsky, 1996; Crocker & Brants, 2000; Narayanan & Jurafsky, 2002
22 July Surprisal and approximate surprisal Lecture 3: catchup and surprisal; inference over infinite tree sets Hale, 2001; Levy, Reali, & Griffiths, 2009 Levy, 2008a; Smith & Levy, 2008; Demberg & Keller, 2008, Boston et al., 2008
23 July Input uncertainty and noisy-channel Bayesian inference in word recognition & sentence comprehension Lecture 4 Levy, 2008b Norris, 2006
24 July Optimality in sentence production; Uniform Information Density Lecture 5 Levy & Jaeger, 2007 Genzel & Charniak, 2002, 2003; Aylett & Turk, 2004; Keller, 2004; Piantadosi, Tily, & Gibson, 2009