IJCAI95 workshop program: learning for language processing

Tue May 9 12:29:31 EDT 1995

                            IJCAI-95 Workshop on

         New Approaches to Learning for Natural Language Processing

     International Joint Conference on Artificial Intelligence (IJCAI-95)
                      Palais de Congres,  Montreal, Canada

                               August 21, 1995

ORGANIZING COMMITTEE
--------------------

Stefan Wermter, University of Hamburg, Germany (workshop contact person)
Gabriele Scheler, Technical University Munich, Germany
Ellen Riloff, University of Utah, USA

INVITED SPEAKERS
----------------

Eugene Charniak, Brown University, USA
Noel Sharkey, Sheffield University, UK

PROGRAM COMMITTEE
-----------------

Jaime Carbonell, Carnegie Mellon University, USA
Joachim Diederich, Queensland University of Technology, Australia
Georg Dorffner, University of Vienna, Austria
Jerry Feldman, ICSI, Berkeley, USA
Walther von Hahn, University of Hamburg, Germany
Aravind Joshi, University of Pennsylvania, USA
Ellen Riloff, University of Utah, USA
Gabriele Scheler, Technical University Munich, Germany
Stefan Wermter, University of Hamburg, Germany

WORKSHOP DESCRIPTION
--------------------

In the last few years, there has been a great deal of interest and activity
in developing new approaches to learning for natural language processing
Various learning methods have been used, including

- connectionist methods/neural networks
- machine learning algorithms
- hybrid symbolic and subsymbolic methods
- statistical techniques
- corpus-based approaches.

In general, learning methods are designed to support automated knowledge
acquisition, fault tolerance, plausible induction, and rule inferences. Using
learning methods for natural language processing is especially important
because language learning is an enabling technology for many other language
processing problems, including  noisy speech/language integration, machine
translation, and information retrieval. Different methods support language
learning to various degrees but, in general, learning is important for
building more flexible, scalable, adaptable, and portable natural language
systems.

This workshop is of interest particularly at this time because systems built
by learning methods have reached a level where they can be applied to
real-world problems in natural language processing and where they can be
compared with more traditional encoding methods. The workshop will provide a
forum for discussing various learning approaches for supporting natural
language processsing. In particular the workshop will focus on questions like:

- How can we apply suitable existing learning methods for language processing?

- What new learning methods are needed for language processing and why?

- What language knowledge should be learned and why?

- What are similarities and differences between different approaches for
  language learning? (e.g., machine learning algorithms vs neural networks)

- What are strengths and limitations of learning rather than manual encoding?

- How can learning and encoding be combined in symbolic/connectionist systems?

- Which aspects of system architectures and knowledge engineering have to
  be considered? (e.g., modular, integrated, hybrid systems)

- What are successful applications of learning methods in various fields?
  (speech/language integration, machine translation, information retrieval)

- How can we evaluate learning methods using real-world language?
  (text, speech, dialogs, etc.)

WORKSHOP PROGRAM
----------------

8:00 am Start of Workshop

8:00 am
Welcome and Introduction
Stefan Wermter

8:10am - 9:50am
Session: Neural network approachs, Hybrid approaches, Genetic approaches
------------------------------------------------------------------------

8:10am - 8:30am
On the applicability of neural network and machine learning methodologies to
natural language processing
Steve Lawrence, Sandiway Fong, C. Lee Giles

8:30am - 8:50am
Knowledge acquisition in concept and document spaces by using self-organizing
neural networks
Werner Winiwarter, Erich Schweighofer, Dieter Merkl

8:50am - 9:10am
A genetic algorithm for the induction of natural language grammars
Tony C. Smith, Ian H. Witten

9:10am - 9:30am
SKOPE: A connectionist/symbolic architecture of spoken Korean processing
Geunbae Lee, J. H. Lee

9:30am - 9:50am
Integrating different learning approaches into a multilingual spoken
translation system
P. Geutner, B. Suhm, T. Kemmp, A. Lavie, L. Mayfield, A. E. McNair, 
I. Rogina, T. Schultz, T. Sloboda, W. Ward, M. Woszczyna, A. Waibel

9:50am - 10:20am
Invited Talk
************

Connectionist Natural Language Processing: Representation and Learning
Noel Sharkey, Sheffield University, UK

10:20am - 10:40am 
Break
-----

10:40am - 12:20am
Session: Statistical approaches, Corpus-based approaches
--------------------------------------------------------

10:40am - 11:00am
Selective sampling in natural language learning
Ido Dagan, Sean P. Engelson

11:00am - 11:20am
Learning restricted probabilistic link grammars
Eva Fong, Dekai Wu

11:20am - 11:40am
A statistical approach to learning prepositional phrase attachment
disambiguation
Alexander Franz

11:40am - 12:00am
Training stochastical grammars on semantic categories
W.R. Hogenhout, Yuji Matsumoto

12:00am - 12:20pm
Automatic classification of speech acts with semantic classification trees and
polygrams
Marion Mast, Elmar Noeth, Heinrich Niemann, Ernst Guenter Schukat Talamazzini

12:20pm - 12:50pm
Invited Talk
************
Learning syntactic disambiguation through word statistics and why you should
care about it
Eugene Charniak, Brown University, USA

12:50pm - 2:00pm
Lunch Break
-----------

2:00pm - 3:40pm
Session: Machine learning appoaches, Symbolic approaches
--------------------------------------------------------

2:00pm - 2:20pm
A comparison of two methods employing inductive logic programming for
corpus-based parser construction
John M. Zelle, Raymond J. Mooney 

2:20pm - 2:40pm
Using inductive logic programming to learn the past tense of English verbs
Mary Elaine Califf, Raymond J. Mooney

2:40pm - 3:00pm
A revision learner to acquire verb selection rules from human-made rules and
examples
Shigeo Kaneda, Hussein Almuallim, Yasuhiro Akiba, Megumi Ishii, Tsukasa
Kawaoka

3:00pm - 3:20pm
Using parsed corpora for circumventing parsing
Aravind K. Joshi, B. Srinivas

3:20pm - 3:40pm
Acquiring and updating hierarchical knowledge for machine translation based on
a clustering technique
Takefumi Yamazaki, Michael J. Pazzani, Christopher Merz

3:40pm - 4:00pm
Break
-----

4:00pm - 5:40pm
Session: Knowledge acquisition approaches, Information extraction approaches
----------------------------------------------------------------------------

4:00pm - 4:20pm
Embedded machine learning systems for natural language processing: a general
framework
Claire Cardie

4:20pm - 4:40pm
Learning information extraction patterns from examples
Scott B. Huffman

4:40pm - 5:00pm
A symbolic and surgical acquisition of terms through variation
Christian Jacquemin

5:00pm - 5:20pm
Concept learning from texts - a terminological meta-reasoning perspective
Udo Hahn, Manfred Klenner, Klemens Schnattinger

5:20pm - 5:40pm
Applying machine learning to anaphora resolution
Chinatsu Aone, Scott William Bennett

5:40pm - 6:00pm
Discussion and open end
-----------------------

Further accepted papers 
-----------------------

Advances in analogy-based learning: false friends and exceptional items in
pronunciation by paradigm-driven analogy
Stefano Federici, Vito Pirrelli, Francais Yvon

A minimum description length approach to grammar inference
Peter Gruenwald

Implications of an automatic lexical acquisition system
Peter M. Hastings

Confronting an existing machine learning algorithm to the text categorization
task
Isabelle Moulinier, Jean-Gabriel Ganascia

Issues in inductive learning of domain-specific text extraction rules
Stephen Soderland, David Fisher, Jonathan Aseltine, Wendy Lehnert

Can punctuation help learning?
Miles Osborne

Ross Hayward, Emanuel Pop, Joachim Diederich
Cascade 2 networks for grammar recognition

********************************************************************************
* Dr Stefan Wermter                          University of Hamburg             *
*                                            Dept. of Computer Science         *
*                                            Vogt-Koelln-Strasse 30            *
* email: wermter at informatik.uni-hamburg.de   D-22527 Hamburg                   *
* phone: +49 40 54715-531                    Germany                           *
* fax:   +49 40 54715-515                                                      *
* http://www.informatik.uni-hamburg.de/Arbeitsbereiche/NATS/staff/wermter.html *
********************************************************************************