From: sml@essex.ac.uk (Lucas S M)
Newsgroups: comp.ai.neural-nets
Subject: GRAMMATICAL INFERENCE WORKSHOP
Date: 19 Dec 92 19:18:02 GMT
Distribution: comp
Organization: University of Essex, UK


   1st ANNOUNCEMENT AND CALL FOR PAPERS
  --------------------------------------

 GRAMMATICAL INFERENCE: THEORY, APPLICATIONS AND ALTERNATIVES
--------------------------------------------------------------

 22-23 April, 1993

  At the UNIVERSITY OF ESSEX,
         WIVENHOE PARK, 
         COLCHESTER CO4 3SQ, UK

 Sponsored by the Institute of Electrical Engineers and the
 Institute of Mathematics.


 Relevant Research Areas:

  *  Computational Linguistics

  *  Machine Learning

  *  Pattern Recognition

  *  Neural Networks

  *  Artificial Intelligence

 MOTIVATION
------------

Grammatical Inference is an immensely important research area
that has suffered from the lack of a focussed  research community.

A two-day colloquium will be held at the University of Essex
on the 22-23rd April 1993.  The purpose of this colloquium is
to bring together researchers who are working on grammatical
inference and closely related problems such as sequence learning
and prediction. 

Papers are sought for the technical sessions listed below.


 BACKGROUND
------------

A grammar is a finite declarative description
of a possible infinite set of data (known as the language)
that is reversible in the sense that it may be used
to detect language membership (or degree of membership) of a pattern,
or it may be used generatively to produce samples
of the language.  

The language may be formal and simple such as the
set of all symmetric strings over a given alphabet,
formal and more complex such as the set of legal PASCAL
programs, less formal such as sentences or phrases in natural language,
or noisy such as vector-quantised speech or 
handwriting, or even spatial rather than temporal,
such as 2-d images.  For the noisy cases
stochastic  grammars are often used that define the
probability that the data was generated by the given
grammar.

So, given a set of data that the grammar is supposed
to generate, and perhaps also a set that it should not
generate, the problem is to learn a grammar that not only 
satisfies these conditions, but more importantly,
generalises to unseen data in some desirable way
(this may be strictly specified in test-cases where the
grammar used to create the training samples is known).

To date, the grammatical inference research community
has evolved largely divided into the following areas 

  a) Theories about the type of languages that can and cannot
be learned.  These theories are generally concerned with the
types of language that may and may not be learned in polynomial
time.  Arguably irrelevant in practical terms since in practical
applications we are usually happy to settle for a good grammar
rather than some `ideal' grammar.

  b) Explicit Inference; this  deals directly with modifiying a 
set of production rules until a satisfactory grammar is obtained.

 c) Implicit inference e.g. estimating the
parameters of a hidden Markov model -- in this case production
rule probabilities in the equivalent stochastic regular
grammar are represented by pairs of numbers in the HMM.

 d) Estimating models where the grammatical
equivalence uncertain (e.g. recurrent neural networks),
but often aim to solve exactly the same problem.

In many cases, researchers in these distinct subfields
seem unaware of the other work in the other subfields;
this is surely detrimental to the progress of grammatical 
inference research.


 TECHNICAL SESSIONS
--------------------

 Oral and poster papers are requested in the following areas: 

 Theory:

What kinds of language are theoretically learnable; the practical import
of such theories.  Learning 2-d and higher-dimensional grammars,
attribute grammars etc.


 Algorithms:

Any new GI algorithms, or new insights on old ones.  Grammatical inference
assistants, that aim to aid humans in writing grammars.  
Performance of Genetic algorithms and simulated annealing 
for grammatical inference etc.

 Applications:

Any interesting applications in natural language processing,
speech recognition
Speech and language processing, cursive script recognition,
pattern recognition, sequence prediction, financial markets etc.

 Alternatives:

The power of alternative approaches to sequence learning,
such as stochastic models and artificial neural networks,
where the inferred grammar may have a distributed rather than an explicit
represention.

 Competition:

A number of datasets will be made available for authors
to report the performance of their algorithms on,
in terms of learning speed and generalisation power.
There is also the possiblity of a live competition
in the demonstration session.

 Demonstration:

There will be a  session
where authors may demonstrate their algorithms.
For this purpose we have a large number of Unix 
workstations running X-Windows, with compilers
for C, C++, Pascal, Fortran, Common Lisp and Prolog.  If your algorithms
are written in a more exotic language, we may still be
able to sort something out.  PCs can be made available if
necessary.


 DISCUSSIONS
-------------

There will be open forum discussions of planning the next
Grammatical Inference Conference, and
the setting up of a Grammatical Inference Journal
(possibly an electronic one).

 PUBLICATIONS
--------------

 Loose-bound collections of accepted conference
papers will be distributed to delegates
upon arrival.  It is planned to publish a selection of
these papers in a book following the conference.


 REMOTE PARTICIPATION
----------------------

 Authors from outside the UK unable to attend the
conference are strongly encouraged to submit
a self-explanatory poster-paper that will be displayed
at the conference.


 SUBMISSION DETAILS
--------------------

 Prospective authors should submit a 2-page abstract
to Simon Lucas at the address below by the end
of February, 1992.  Email and Faxed abstracts
are acceptable.  Notification of the intention
to submit an abstract would would also be 
appreciated.


 REGISTRATION DETAILS
----------------------

 Prospective delegates are requested to mail/email/fax
at the address below for further details.




--

------------------------------------------------
Dr. Simon Lucas
Department of Electronic Systems Engineering
University of Essex
Colchester CO4 3SQ
United Kinkdom

Tel:    0206 872935
Fax:    0206 872900
Email:  sml@uk.ac.essex
-------------------------------------------------
Newsgroups: comp.ai.neural-nets