Connectionist symbol processing: any progress

[Stefan Wermter]

Jamie Henderson writes:

>  - Connectionist approaches to processing structural information have
made
>        significant progress, to the point that they can now be
justified on
>        purely empirical/engineering grounds.
>  - Connectionist methods do solve problems that current
non-connectionist
>        methods have (ad-hoc independence assumptions, sparse data,
etc.),
>        and people working in learning know it.
>  - Connectionist NLP researchers should be using modern empirical
methods,
>        and they will be taken seriously if they do.


I would support Jamie Hendersons view. While it might
have been the state of the art 10 years ago to focus on
small single networks for toy tasks in isolation, there has
been an interesting development of using connectionist
networks not only for cognitive modeling but for instance
for (language) engineering. Larger modular architectures
have been explored (for instance there were several
recent issues on modular architectures in the journal
connection science, guest-edited by A. Sharkey) and
neural networks might also be used in context with
other modules in larger systems. And it is useful and
necessary to compare with traditional well-known
techniques, e.g. n-grams, etc

In some of our recent work on the screen system for instance,
we have processed  speech input from acoustics
over syntax and semantics up to dialog levels based on
two corpora of several thousand words. All the main
processing could be done with neural networks in a modular
architecture for a speech/language system. So connectionist
techniques are not only  useful for modeling specific
cognitive constraints well but can also be used successfully
for larger tasks like learning text tagging or learning spoken
language analysis. Below some references if interested.


Wermter S., Weber, V. 1997. SCREEN: Learning a flat syntactic
and semantic spoken language analysis using artificial neural
networks, Journal of Artificial Intelligence Research 6(1) p. 35-85

Wermter, S. Riloff, E. Scheler, G. (Ed). 1996. Connectionist,
Statistical and Symbolic Approaches to Learning
 for Natural Language Processing Springer Verlag, Berlin.

Wermter S., Meurer M. 1997. Building lexical representations
dynamically using artificial neural networks. Proceedings of the
International Conference of the Cognitive Science Society,
p. 802-807, Stanford.


I would be interested to hear if anybody working on neural network
techniques has recently developed MODULAR  neural  techniques in other
fields, e.g. for integrating vision and speech, data/text mining,
intelligent controllers, learning web agents, neuro-fuzzy reasoning,
information extraction and information retrieval or other
forms of intelligent processing.

best wishes,

Stefan

********************************************
Professor Stefan Wermter
Research Chair in Intelligent Systems
University of Sunderland
Dept. of Computing & Information Systems
St Peters Way
Sunderland SR6 0DD
United Kingdom

phone: +44 191 515 3279
fax:   +44 191 515 2781
email: stefan.wermter at sunderland.ac.uk
http://osiris.sunderland.ac.uk/~cs0stw/
********************************************