LINGUIST List 12.1313

Sat May 12 2001

Calls: Information Processing, Machine Learning

Editor for this issue: Jody Huellmantel <jodylinguistlist.org>


As a matter of policy, LINGUIST discourages the use of abbreviations or acronyms in conference announcements unless they are explained in the text.

Directory

  1. Priscilla Rasmussen, Temporal & Spatial Information Processing (ACL 2001)
  2. Priscilla Rasmussen, Special Issue of the Journal of Machine Learning Research

Message 1: Temporal & Spatial Information Processing (ACL 2001)

Date: Fri, 11 May 2001 16:32:49 EDT
From: Priscilla Rasmussen <rasmussecs.rutgers.edu>
Subject: Temporal & Spatial Information Processing (ACL 2001)


*** CALL FOR PARTICIPATION IN TEMPORAL AND SPATIAL INFORMATION PROCESSING***

 An ACL'2001 Workshop to be held in Toulouse, France
 July 7, 2001
 URL: http://www.irit.fr/ACTIVITES/EQ_ILPL/aclWeb/acl2001.html

REGISTRATION: see
http://www.irit.fr/ACTIVITES/EQ_ILPL/aclWeb/acl2001.html
 * Early registration: by May 24 (lower registration fee)
 * Late registration: May 25 - June 24
 * After June 24th, registration will be on site only

PRELIMINARY PROGRAM
- -----------------
http://epsilon3.georgetown.edu/~discours/spacetime.html

INVITED SPEAKERS
- -------------
Fabio Pianesi, ITC-IRST, Italy
Barbara Tversky, Stanford University, USA

SPONSORS
- ------ 

MITRE 
ACL SIGMEDIA 
Automatic Content Extraction (ACE) Program 


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

Elisabeth Andre, DFKI, Germany 
Myriam Bras, IRIT, France 
Rob Gaizauskas, Sheffield, UK 
Udo Hahn, Freiburg University, Germany 
Eduard Hovy, USC-ISI, USA 
G=E9rard Ligozat, LIMSI-CNSRS, France, 
Ruslan Mitkov, University of Wolverhampton, UK 
Marc Moens, University of Edinburgh, UK 
Dragomir Radev, University of Michigan, USA 
Ellen Riloff, University of Utah, USA 
Laure Vieu, IRIT, France 
Clare Voss, US Army Research Lab, USA
Michael White, Cogentex, USA 
Janyce Wiebe, University of Pittsburgh, USA 
George Wilson, MITRE, USA 
Cornelia Zelinsky-Wibbelt, Hannover, Germany 


ORGANIZERS 

- --------
Lisa Harper, MITRE, USA 
Inderjeet Mani, MITRE and Georgetown University, USA 
Beth Sundheim, SPAWAR Systems Center, USA
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Message 2: Special Issue of the Journal of Machine Learning Research

Date: Fri, 11 May 2001 16:48:35 EDT
From: Priscilla Rasmussen <rasmussecs.rutgers.edu>
Subject: Special Issue of the Journal of Machine Learning Research


 Call for Papers: Special Issue of the Journal of Machine Learning
 Research -- "Machine Learning Approaches to Shallow Parsing"

Editors: James Hammerton james.hammertonucd.ie, University College Dublin
	 Miles Osborne osbornecogsci.ed.ac.uk, University of Edinburgh
	 Susan Armstrong susan.armstrongissco.unige.ch, University of Geneva
	 Walter Daelemans walter.daelemansuia.ua.ac.be, University of Antwerp

The Journal of Machine Learning Research invites authors to submit
papers for the Special Issue on Machine Learning approaches to Shallow
Parsing.

Background
- --------

Over the last decade there has been an increased interest in applying
machine learning techniques to corpus-based natural language
processing. In particular many techniques have been applied to shallow
parsing of large corpora, where rather than produce a detailed
syntactic or semantic analysis of each sentence, key parts of the
syntactic structure or key pieces of semantic information are
identified or extracted. For example, such tasks include identifying
the noun phrases in a text, extracting non-overlapping chunks of text
that identify the major phrases in a sentence or extracting the
subject, main verb and object from a sentence. 

Applications of shallow parsing include data mining from unstructured
textual material (e.g. web pages, newswires), information extraction,
question answering, automated annotation of linguistic corpora and the
preprocessing of data for linguistic tasks such as machine translation
or full scale parsing.

Shallow parsing of realistic, naturally occuring language poses a number
of challenges for a machine learning system. Firstly, the training set
is usually large which will push batch techniques to the limit. The
training material is often noisy and frequently only partially
determines a model (that is, only some aspects of the target model are
observed). Secondly, shallow parsing requires making large numbers
of decisions which translates as learning large models. The size of
such models usually results in extremely sparse counts, which makes
reliable estimation difficult. In sum, learning how to do shallow
parsing will tax almost any machine learning algorithm and will thus
provide valuable insight into real-world performance. 

In a number of workshops and publications, a variety of machine
learning techniques have been applied in this area including memory
based (instance based) learning, inductive logic programming,
probabilistic context free grammars, maximum entropy, transformation
based learning, artificial neural networks and more recently support
vector machines. However there has not been an opportunity to
compare and contrast these techniques in a systematic manner. The
special issue will thus provide a venue for drawing together the relevant
ML techniques. 

TOPICS
- ----

The aim of the special issue is to solicit and publish papers that
provide a clear view of the state of the art in machine learning for
shallow parsing. We therefore encourage submissions in the following
areas:

* applications of machine learning techniques to shallow parsing
tasks, including the development of new techniques.

* comparisons of machine learning techniques for shallow parsing

* analyses of the complexity of machine learning for shallow
parsing tasks

To facilitate cross-paper comparison and thus strengthen the special
issue as a whole, authors are encouraged to consider using one of the
following data sets provided via the CoNLL workshops (please note
however that this is not mandatory):

http://lcg-www.uia.ac.be/conll2000/chunking/

or:

http://lcg-www.uia.ac.be/conll2001/clauses/

We emphasise that authors will not be solely judged in terms of raw
performance and this is not to be considered as a competition: insight
into the strengths and weaknesses of a given system is deemed to be
more important.

High quality papers reviewing machine learning for shallow parsing
will also be welcome. 

Instructions
- ----------

Articles should be submitted electronically. Postcript or PDF format
are acceptable and submissions should be single column and typeset in
11 pt font format, and include all author contact information on the
first page. See the author instructions at www.jmlr.org for more
details. 

To submit a paper send the normal emails asked for by the JMLR in
their author instructions to submissionsjmlr.org (NOT to the editors
directly), indicating in the subject headers that the submission is
intended for the Special Issue on Machine Learning Approaches to
Shallow Parsing.

Key dates
- -------

Submission deadline: 2nd September 2001

Notification of acceptance: 16th November 2001

Final drafts: 3rd February 2002

Further information
- -----------------

Please contact James Hammerton <james.hammertonucd.ie> with any queries.


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