Machine Learning and Cognitive Science of Language Acquisition
A PASCAL core event
An interdisciplinary workshop bringing together researchers in cognitive
science and machine learning who are interested in language acquisition.
(http://www.cs.rhul.ac.uk/home/alexc/coglang/index.html)
Sponsored by the PASCAL network of excellence in Machine Learning.
(www.pascal-network.org)
Location: University College, London
Date: 21 and 22 June 2007, (Thursday and Friday)
Organisers
Alex Clark, Department of Computer Science, Royal Holloway University of London
Nick Chater, Department of Psychology, University College London
Invited Speakers:
John Goldsmith (Linguistics, University of Chicago)
Chris Manning (Computer Science and Linguistics, Stanford University)
Morten Christiansen (Psychology, Cornell University)
Matthew Crocker (Psycholinguistics, Saarland University)
Walter Daelemans (Computational Linguistics and Artificial Intelligence,
University of Antwerp)
Colin de la Higuera (Grammatical Inference, St Etienne)
Language acquisition and processing has been one of the central research
issues in cognitive science. It is also an area in which the use of
cognitive computational modelling has been especially intense.
Language, and especially language acquisition, has been the key
battleground for nativists and empiricists; and between advocates of
rule-based, probabilistic, and connectionist models of thought. Yet the
computational models proposed by CogSci researchers are often far
behind, in scale and accuracy, the non-cognitively motivated models
proposed by computational linguists, which are heavily based on machine
learning techniques.
This workshop asks how far these techniques, and their theoretical
underpinnings, provide tools for building richer theories of cognitive
processes.
For example, can powerful machine learning techniques
(e.g. kernel methods) help build models of the cognitive operations
involved in human language acquisition? Conversely, can insights from
cognitive science help inform and focus computational linguistic and
machine learning? Can evidence concerning the spectacular computational
performance of the human language processor help inspire new generations
of computational linguistic and machine learning tools?
This workshop will bring together participants from all of the disciplines
that address this problem to discuss a range of related topics from
methodological issues in computational modelling of language acquisition,
including evaluation of empirical learning models, to technical problems
in machine learning and grammatical inference.
The workshop includes invited talks by some of the leading researchers in
these fields.
Intended audience
Cognitive scientists with an interest in language and computational
modelling,
Grammatical inference researchers interested in natural language
Computational linguists interested in unsupervised learning of natural
language
Machine learning researchers interested in modelling sequential data, or
tree-structured data, using Bayesian, kernel-based or graphical models.
Linguists interested in computational models of language acquisition.
Psycholinguists with unexplained experimental data looking
for computational models.
COLT or ALT style researchers working on formal models of learning
language.
Format:
The workshop will consist of a mixture of long invited talks, short talks,
and a poster session.
Please submit a 1 page abstract on a topic related to the meeting by email
to Alex Clark (alexc@cs.rhul.ac.uk) or Nick Chater (n.chater@ucl.ac.uk) by
March 30th 2007;
Please indicate your preference for oral or poster presentation.
Notifications of acceptance will be sent out by 18th April 2007.
-- Alexander Clark alexc@cs.rhul.ac.uk http://www.cs.rhul.ac.uk/home/alexc/ Lecturer, Department of Computer Science, Royal Holloway, University of London, Egham, Surrey TW20 0EX Direct 01784 443430 Department 01784 434455 Fax 01784 439786
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