book on language learning: connectionist statistical symbolic approaches

Wed Mar 13 10:42:21 EST 1996

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BOOK ANNOUNCEMENT
-----------------

Title:   
         Connectionist, statistical, and symbolic approaches
         to learning for natural language processing

Editors: 
         Stefan Wermter
         Ellen Riloff
         Gabriele Scheler

Date:    
         March 1996 (first week in Europe

[order information and WWW reference for the book (access to first chapter) 
at the end of this message]

Brief description
-----------------

The purpose of this book is to present a collection of papers that 
represents a broad spectrum of current research in learning methods 
for natural  language processing, and to advance the state of the art 
in language learning and artificial intelligence. The book should bridge 
a gap between several areas that are usually  discussed separately, 
including connectionist, statistical, and symbolic methods. 

Table of contents
-----------------

Introduction:

Learning approaches for natural language processing
S. Wermter, E. Riloff, G. Scheler

Part 1: Connectionist Networks and Hybrid Approaches
----------------------------------------------------

Separating learning and representation
N.E. Sharkey, A.J.C. Sharkey

Natural language grammatical inference: a comparison of recurrent 
neural networks and machine learning methods
S. Lawrence, S. Fong, C. L. Giles

Extracting rules for grammar recognition from Cascade-2 networks
R. Hayward, A. Tickle, J. Diederich

Generating English plural determiners from semantic representations:
a neural network learning approach
G. Scheler

Knowledge acquisition in concept and document spaces by using 
self-organizing neural networks
W. Winiwarter, E. Schweighofer, D. Merkl

Using hybrid connectionist learning for speech/language analysis
V. Weber, S. Wermter

SKOPE: A connectionist/symbolic architecture of spoken Korean 
processing
G. Lee, J.-H. Lee

Integrating different learning approaches into a multilingual spoken
language translation system
P. Geutner, B. Suhm, F.-D. Buo, T. Kemp, L. Mayfield, A. E. McNair, 
I. Rogina, T. Schultz, T. Sloboda, W. Ward,  M. Woszczyna, A. Waibel

Learning language using genetic algorithms
T. C. Smith, I. H. Witten

Part 2: Statistical Approaches
---------------------------------------------------

A statistical syntactic disambiguation program and what it learns
M. Ersan, E. Charniak

Training stochastic grammars on semantical categories
W.R. Hogenhout, Y. Matsumoto

Learning restricted probabilistic link grammars
E. W. Fong, D. Wu

Learning PP attachment from corpus statistics
A. Franz

A minimum description length approach to grammar inference
P. Gruenwald

Automatic classification of dialog acts with semantic classification
trees and polygrams
M. Mast, H. Niemann, E. Noeth, E. G. Schukat-Talamazzini

Sample selection in natural language learning
S. P. Engelson, I. Dagan

Part 3: Symbolic Approaches
---------------------------------------------------

Learning information extraction patterns from examples
S. B. Huffman

Implications of an automatic lexical acquisition system
P. M. Hastings

Using learned extraction patterns for text classification
E. Riloff

Issues in inductive learning of domain-specific text extraction 
rules
S. Soderland, D. Fisher, J. Aseltine, W. Lehnert

Applying machine learning to anaphora resolution
C. Aone, S. W. Bennett

Embedded machine learning systems for natural language processing: 
a general framework
C. Cardie

Acquiring and updating hierarchical knowledge for machine translation
based on a clustering technique
T. Yamazaki, M. J. Pazzani, C. Merz

Applying an existing machine learning algorithm to text 
categorization
I. Moulinier, J.-G. Ganascia

Comparative results on using inductive logic programming for 
corpus-based parser construction
J. M. Zelle, R. J. Mooney 

Learning the past tense of English verbs using inductive logic 
programming
R. J. Mooney, M. E. Califf  

A dynamic approach to paradigm-driven analogy
S. Federici, V. Pirrelli, F. Yvon

Can punctuation help learning?
M. Osborne

Using parsed corpora for circumventing parsing
A. K. Joshi, B. Srinivas

A symbolic and surgical acquisition of terms through variation
C. Jacquemin

A revision learner to acquire verb selection rules from human-made 
rules and examples
S. Kaneda, H. Almuallim, Y. Akiba, M. Ishii, T. Kawaoka

Learning from texts - a terminological metareasoning perspective
U. Hahn, M. Klenner, K. Schnattinger

**************************************************************

Bibliographic Data and Ordering Information:

Editors: Stefan Wermter, Univ. of Hamburg, Germany
         Ellen Riloff, Univ. of Utah, Salt Lake City, USA
         Gabriele Scheler, Munich Univ. of Tech. Germany

Title:   Connectionist, Statistical, and Symbolic Approaches
         to Learning for Natural Language Processing

Publisher: Springer-Verlag

ISBN:    3-540-60925-3

Pages:   468 + 9

Available: Europe: March 6, 1996
           North America: around March 25, 1996

Subseries: Lecture Notes in Artificial Intelligence
           LNAI 1040

Cover: Softcover under Color Jacket Cover

List Price: DM 86.00, approx. USD 68.00

With this information, any academic bookseller worlwide
with a resonable computer science program should be able
to provide copies of the book. Otherwise, one also can
order through any Springer office directly, particularly
through Berlin and Secaucus, as mentioned in the following
special offer to Springer Authors. If you aren't a Springer
Author you aren't entitled to make use of the special
discount, but the ordering addresses are the same.

**********************************************************

SPECIAL OFFER: SPRINGER-AUTHOR DISCOUNT

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******************************************************************************
*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*
******************************************************************************