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Lectures with Tutorials (4 SWS, 6LP)
MSc in LS&T, Diplom CL, BA in CL
Leitung: Matthew Crocker & Marshall Mayberry
Mon 14-16, Wed 14-16, Geb. 17.1, Besprechungsraum U.15
Tutorials will be held sometimes in the CIP-Raum
Lectures and readings will be in English
Begin: Monday, 29 October 2007
Course Contents
This course will discuss current computational models of human language processing. We will consider both how computational linguistics can inform the development of psycholinguistic theories, and also how computational models can account for and explain (experimentally) observed human language processing behaviour. The course will begin with an introduction to psycholinguistic research, summarising both the key observations about human language understanding, and also presenting central theoretical debates including issues such as modularity, incrementality, and the psychological status of linguistic principles and representations. We will then consider a number of computational models of lexical and sentence level processing and language acquisition. The models covered exploit symbolic, probabilistic, connectionist, and also 'hybrid' computational mechanisms.
|
Week |
Monday |
Wednesday |
|
1 |
Introduction: modularity, competence-performance, incrementality, ambiguity resolution and garden-path phenomena. [Lecture1.pdf] |
Tutorial 1: Intro to COGENT, experiment modeling |
| 2 | Parsing and psychological reality: incrementality, memory load, and disambiguation. Implementing top-down, shift-reduce, and left-corner models. [Lecture2.pdf] |
Tutorial 2: Using the COGENT Framework for Sentence Parsing. Shift-reduce parsing. |
|
3 |
Theories of Parsing: Grammar based accounts of parsing & theories of reanalysis. [Lecture3.pdf, Trees.pdf] |
Tutorial 3: Top-down parsing. |
|
4 |
Reanalysis : long-distance dependencies, monotonic parsing and reanalysis.[Lecture4.pdf] |
Tutorial 4: Continuation of Tutorial 3. |
|
5 |
Probabilistic Models 1: Experience-based theories, probabilities and rational analysis. [Lecture5.pdf] |
Tutorial 5: Left-corner parsing in Prolog & Memory Load |
|
6 |
Probabilistic Models 2: Probabilistic models of lexical processing (Corley & Crocker, 2002). PCFGs, Jurafsky (1996). [Lecture6.pdf] |
Tutorial 6: COGENT projects. |
|
7 |
Interactive Models: The interactive-activation model, and the competition-integration model [Lecture7.pdf] |
Introduction to Connectionist Models [Lecture8.pdf] |
|
8 |
Learning Neural Networks [Lecture9.pdf] |
Tutorial 7: Supervision of COGENT parsing projects |
| 9 | Tutorial 8: COGENT support available | Tutorial 9: Presentation of COGENT parsing projects |
|
10 |
Pattern Associators and Competitive Networks [Review, Lecture10.pdf] |
Tutorial 10: Activation propagation and Network Specification in Tlearn. |
|
11 |
Learning Phonology and Morphology [Lecture11.pdf] |
Tutorial 11: Reading aloud. |
|
12 |
Simple recurrent networks: processing sequences [Lecture12.pdf] |
Tutorial 12: The English past-tense. |
|
13 |
SRNs: Learning syntax, and starting small [Lecture13.pdf] |
Tutorial 13: Pattern Association |
|
14 |
Course Overview, Q & A [Q&A.pdf] |
Tutorial 14: SRNs |
|
15 |
Exam: Friday, February 22, 2008 @ 14:00 *sharp* |
|
Tutorials
For details about the tutorials, please keep an eye on Garance's tutorial page.
Software
The course will use two systems for experimenting with computational models of human language processing.
Both systems are freely available, for Mac OS, Linux, and Windows operating systems.
Essential Readings
Richard Cooper. Modelling High-Level Cognitive Processes. Lawrence Earlbaum, Mahwah, NL, 2002.
Matthew Crocker. Mechanisms for Sentence Processing. In: Garrod & Pickering (eds), Language Processing, Psychology Press, London, UK, 1999.
Dan Jurafsky. Probabilistic Modeling in Psycholinguistics. In Bod et al (eds.). Probabilistic Linguistics. The MIT Press, 2003.
P. McLeod, K. Plunkett and E. T. Rolls (1998). Introduction to Connectionist Modelling of Cognitive Processes. Oxford University Press. Chapters: 1-5, 7, 9.
Ken McRae, Michael Spivey-Knowlton, Michael Tanenhaus. Modeling the Influence of Thematic Fit (and Other Constraints) in On-line Sentence Comprehension. Journal of Memory and Language, 38, 283–312 (1998)
K. Plunkett and J. Elman (1997). Exercises in rethinking innateness: A Handbook for Connectionist Simulations. MIT Press. Chapters: 1-8, 11, 12.
Additional Literature
N. Chater and M. Christiansen (1999). Connectionism and natural language processing. Chapter 8 of Garrod and Pickering (eds.): Language Processing. Psychology Press.
M. Christiansen and N. Chater (1999). Connectionist Natural Language Processing: The State of the Art. Cognitive Science, 23(4): 417-437.
Matthew Crocker and Steffan Corley. Modular Architectures and Statistical Mechanisms: The Case from Lexical Category Disambiguation. In: Merlo & Stevenson (eds), The Lexical Basis of Sentence Processing, John Benjamins, Amsterdam (in press).
Matthew Crocker and Thorsten Brants. Wide Coverage Probabilistic Sentence Processing. Journal of Psycholinguistic Research; 29(6):647-669, 2000.
J. Elman et al. (1996). Chapter 2: Why Connectionism? In: Rethinking Innateness. MIT Press.
Daniel Jurafsky. A Probabilistic Model of Lexical and Syntactic Access and Disambigiuation. Cognitive Science, 20, 137-194 (1996).
J. Elman (1990). Finding Structure in Time. Cognitive Science, 14: 179-211.
J. Elman (1991). Distributed representations, simple recurrent networks, and grammatical structure. Machine Learning, 7: 195-225.
J. Elman (1993). Learning and development in neural networks: The importance of starting small. Cognition, 48: 71-99.
M. Seidenberg and M. MacDonald (1999). A Probabilistic Constraints Approach to Language Acquisition and Processing. Cognitive Science, 23(4): 569-588.
M. Steedman (1999). Connectionist Sentence Processing in Perspective. Cognitive Science, 23(4): 615-634.