Lecturer: Matthew Crocker
Format: Lectures with Tutorials (4 SWS, 6LP)
Programme: MSc in LS&T, Diplom CL, BA in CL
Times: Mon 14-16 (Lecture), Wed 14-16 (Tutorial)
Location: On-line in Microsoft Teams
Language of Instruction: English
Course begin: Monday, 25 Oct 2021
Course Organisation for Winter 2021-21
For at least the beginning of the course, lectures and tutorials will take place on-line, using MS Teams, as follows:
- Lectures will be done on-line in MS Teams during the Monday lecture slot (occasionally, possible pre-recorded).
- Tutorial sheet will be posted in Teams, after each lecture: please get as far as you can with the tutorial before the Tutorial slot.
- Tutorials usually take place on Wed at 14:15 in Teams, and you are expected to participate in all sessions.
- Tutorials will provided an opportunity for any further Q&A about lectures, and assistance in progressing with the tutorials.
- Completed tutorial sheets must be submitted by midnight on Sundays, before the next lecture, via Teams. You must hand in all tutorials.
- All course lectures, links, downloads, and assignments will be distributed via Teams: here [Link to Teams] .
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.
Tutorials
Files for the tutorials will be posted via Teams
Software
The course will use several systems for experimenting with computational models of human language processing.
- Prolog implementations of incremental parsers. You can get SWI-Prolog [here], and find online tutorials [here]
- Probabilistic models of lexical and syntactic processing
Systems are freely available, for Mac OS, Linux, and Windows operating systems. You will need a login for the Coli servers, and you will need to run an X-terminal on your computer, to enable the use of graphical software on the servers. Please familiarize yourself with the basic Unix command line, and a text editor like vi, vim, or emacs.
Course Readings
- Matthew Crocker. Computational Psycholinguistics. In: Clark, Fox & Lappin (eds), Handbook of Computational Linguistics and Natural Language Processing, Blackwell, London, UK, 2010.
- 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.
- John Hale. A Probabilistic Earley Parser as a Psycholinguistic Model. Proceedings of the ACL, 2001.
- 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).
- 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.
Additional Literature (not relevant for 2015-16).
- 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.
- 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.
- P. McLeod, K. Plunkett and E. T. Rolls (1998). Introduction to Connectionist Modelling of Cognitive Processes. Oxford University Press. Chapters: 1-5, 7, 9.
- K. Plunkett and J. Elman (1997). Exercises in rethinking innateness: A Handbook for Connectionist Simulations. MIT Press. Chapters: 1-8, 11, 12.
- 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.