Computational Psycholinguistics

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: C7.1 Room 1.08
Language of Instruction: English
Course begin: Monday, 22 Oct 2018

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: human performance, competence-performance, modeling. [Lecture1] Tutorial 0: Introduction to Prolog. [Prolog Primer] [Tutorial 0 Files]
2 Parsing and Psychological Reality: incrementality, memory load, and disambiguation. Implementing top-down, shift-reduce, and left-corner models. [Lecture2] No tutorial
3 Tutorial 1: SR Parsing in Prolog.[Tutorial 1 Files] Tutorial 2: Parsing in Prolog (continued). [Tutorial 2 Files]
4 Syntactic processing 1: Modular theories (Frazier, Pritchett). [Lecture3] No tutorial
5 Syntactic processing 2: Reanalysis, Long-distance dependencies. [Lecture4] Tutorial 3: Parsing in Prolog (continued). [Tutorial 3 Files]
6 Probabilistic Models 1: Rational approaches to language processing, category disambiguation. [Lecture5] Tutorial 4: Statistical lexical category disambiguation. [Tutorial 4 Files]
7 Probabilistic Models 2: Probabilistic models of category disambiguation, continued. [Lecture6] Tutorial 5: Statistical lexical category disambiguation, continued. [Tutorial 5 Files]
8 Probabilistic Parsing 1: Jurafsky, Brants and Crocker. [Lecture7] Tutorial 6: Probabilistic parsing with the Roark parser. [Tutorial 6]
9 Probabilistic Parsing 2: Crocker and Brants, Informativity. [Lecture8] Tutorial 7: Incremental parsing with the Roark parser. [Tutorial 7]
10 Break Break
11 Break Break
12 Rational analysis: Surprisal and Prediction Theory. [Lecture9] Tutorial 7: Probabilistic parsing with the Roark parser, continued.[Tutorial 8]
13 Constraint-based Models 1: McRae et al. [Lecture10] [McRae et al, 1998] Tutorial 9: McRae model [Tutorial 9]
14 Constraint-based Models 2: The Ambiguity Advantage. [Lecture11] Tutorial 9: Green & Mitchell. [Tutorial 10]
15 Course review. [Lecture12] Question session
16 EXAM: Mon, February 4, 2019 @ 14:00

Files for the tutorials will appear here, as the course goes on

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.

Course Readings

Additional Literature (not relevant for 2015-16).