13.426, Calls: Computational Ling, Computational Ling
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LINGUIST List: Vol-13-426. Sun Feb 17 2002. ISSN: 1068-4875.
Subject: 13.426, Calls: Computational Ling, Computational Ling
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1)
Date: Thu, 14 Feb 2002 18:05:25 +0100
From: Alessandro Lenci <lenci at ilc.pi.cnr.it>
Subject: Linguistic Knowledge Acquisition and Representation: EXTENDED DEADLINE
2)
Date: Thu, 14 Feb 2002 18:30:59 +0100
From: Sandra Kuebler <kuebler at sfs.uni-tuebingen.de>
Subject: Second CfP: Workshop on Machine Learning Approaches in Computational Linguistics (ESSLLI-2002)
-------------------------------- Message 1 -------------------------------
Date: Thu, 14 Feb 2002 18:05:25 +0100
From: Alessandro Lenci <lenci at ilc.pi.cnr.it>
Subject: Linguistic Knowledge Acquisition and Representation: EXTENDED DEADLINE
********************** NEW DEADLINE: 25th of February 2002 **************
LREC 2002 Workshop on
LINGUISTIC KNOWLEDGE ACQUISITION AND REPRESENTATION:
BOOTSTRAPPING ANNOTATED LANGUAGE DATA
Las Palmas, Canary Islands, Spain
2nd June 2002
********************** NEW DEADLINE: 25th of February 2002 **************
_____________________________
MOTIVATION AND AIMS
Provision of large-scale labelled language resources, such as tagged
corpora or repositories of pre-classified text documents, is a crucial
key to steady progress in an extremely wide spectrum of research,
technological and business areas in the HLT sector. The continuously
changing demands for language-specific and application-dependent
annotated data (e.g. at the syntactic or at the semantic level),
indispensable for design validation and efficient software
prototyping, however, are daily confronted by the labelled-data
bottleneck. Hand-crafted resources are often too costly and
time-consuming to be produced at a sustainable pace, and, in some
cases, they even exceed the limits of human conscious awareness and
descriptive capability.
Possible ways to circumvent, or at least minimise, this problem come
from the literature on automatic knowledge acquisition and, more
generally, from the machine-learning community. Annotated data are
bootstrapped by training a machine-learning classifier with a small
sample of pre-annotated data and by using the induced classifier to
annotate more data. Co-learning provides an alternative methodology,
which essentially consists in iterative cooperation of two or more
independent learning systems. Another promising route consists in
automatically tracking down recurrent knowledge patterns in
unstructured or implicit information sources (such as free texts or
machine readable dictionaries) for this information to be moulded into
explicit representation structures (e.g. subcategorisation frames,
syntactic-semantic templates, ontology hierarchies etc.).
We believe that all these attempts at bootstrapping labelled data are
not only of practical interest (for continuous updating, management
and validation of dynamic resources), but also point to a bunch of
germane theoretical issues. In particular, the workshop intends to
focus on the issue of interaction between techniques for inducing
structured knowledge from raw data and formal methods of linguistic
knowledge representation. Gaining insights into this issue is an
essential requirement for explaining the effective use of linguistic
knowledge by cognitive agents. Although the cognitive and engineering
views of the form and acquisition of linguistic knowledge need not be
related, data from neuroscience and psychology are indeed relevant
when evaluating different ways of representing information in
artificial systems, and different models for linguistic knowledge
acquisition.
We encourage in-depth analysis of underlying assumptions of the
proposed bootstrapping methods and discussion of possible relevant
connections with existing annotation and representation schemes. This
investigation is likely to have significant repercussions on the way
linguistic resources will be designed, developed and used for
applications in the years to come. As the two aspects of knowledge
representation and acquisition are profoundly interrelated, progress
on both fronts can only be achieved, in our view of things, through a
full appreciation of this deep interdependency.
TOPICS OF INTEREST
Possible themes for contributions are:
* development of 'data-driven' annotation/representation schemes
* dynamic update, customisation and tuning of labelled resources
through acquired data
* 'hybrid models' of linguistic knowledge extraction, whereby machine
learning methods are integrated with formal structures of knowledge
representation
* incremental linguistic knowledge-bases
* formal representation and structuring of information flow
automatically acquired from texts
* knowledge acquisition and linguistic resources lifecycle
* linguistic knowledge acquisition and representation in cognitive tasks
NEW!!! IMPORTANT DATES NEW!!!
Deadline for workshop abstract submission:
25th of February 2002
Notification of acceptance:
20th of March 2002
Final version of paper for workshop proceedings:
20th of April 2002
Workshop:
2nd June 2002 (afternoon session)
SUBMISSIONS
The organizers welcome contributions describing existing research
related to the topics of the workshop. Each presentation will be 25
minutes long (20 minutes for presentation and 5 minutes for questions
and discussion). Submissions should include: title; author(s);
affiliation(s); and contact author's e-mail address, postal address,
telephone and fax numbers. Abstracts (maximum 500 words, plain-text
format) must be sent to: simo at ilc.pi.cnr.it
The final version of the accepted papers should not be longer than
4,000 words or 10 A4 pages. Instructions for formatting and
presentation of the final version will be sent to authors upon
notification of acceptance.
ORGANISING COMMITEE
Alessandro Lenci (Universit=E0 di Pisa, Italy)
Simonetta Montemagni (Istituto di Linguistica Computazionale - CNR, Italy)
Vito Pirrelli (Istituto di Linguistica Computazionale - CNR, Italy)
PROGRAM COMMITTEE
Harald Baayen (Max Planck Institute for Psycholinguistics - Nijmegen)
Rens Bod (University of Amsterdam, Holland)
Michael R. Brent (Washington University, USA)
Nicoletta Calzolari (Istituto di Linguistica Computazionale - CNR, Italy)
Jean-Pierre Chanod (Xerox Research Centre Europe, Grenoble, France)
Walter Daelemans (University of Antwerp, Belgium)
Dekang Lin (University of Alberta, Edmonton, Canada)
Horacio Rodriguez (Universidad Politecnica de Catalunya)
Fabrizio Sebastiani (Istituto per l'Elaborazione dell'Informazione -
CNR, Italy)
Lucy Vanderwende (Microsoft Research, Redmond, USA)
Francois Yvon (Ecole Nationale Superieure des Telecommunications,
Paris Frances)
Menno van Zaanen (University of Amsterdam, The Netherlands)
CONTACT PERSON
Simonetta Montemagni
Istituto di Linguistica Computazionale (ILC) - CNR
Area della Ricerca di Pisa
Via Moruzzi 1, 56124 Pisa, ITALY
e-mail: simo at ilc.pi.cnr.it
-------------------------------- Message 2 -------------------------------
Date: Thu, 14 Feb 2002 18:30:59 +0100
From: Sandra Kuebler <kuebler at sfs.uni-tuebingen.de>
Subject: Second CfP: Workshop on Machine Learning Approaches in Computational Linguistics (ESSLLI-2002)
ESSLLI-2002 Workshop on
Machine Learning Approaches in Computational Linguistics
August 5-9, 2002
A workshop held as part of the
14th European Summer School in Logic, Language and Information
ESSLLI-2002
Trento, Italy
August 5-16, 2002
** Second CALL FOR PAPERS **
ORGANIZERS: Erhard Hinrichs, Sandra Kuebler (Universitaet Tuebingen)
DESCRIPTION:
Over the last decade, machine learning approaches have established
themselves as an important subfield of computational linguistics. The
resulting body of research is characterized by a wide range of
techniques. These techniques have been successfully applied to a
variety of natural language annotation tasks, such as part-of-speech
tagging, shallow and deep parsing, word sense disambiguation, anaphora
resolution, PP attachment.
The purpose of this workshop is to provide a forum for these junior
researchers to present their work and discuss the relative merits of
their machine learning approaches as they apply to natural language
phenomena.
Possible learning approaches are:
* inductive logic programming
* memory-based learning
* transformation-based learning
* decision trees
* genetic algorithms
* connectionist learning
SUBMISSION:
All researchers in the area, but especially Ph.D. students and young
researchers, are invited to submit a paper. Electronic submissions are
highly encouraged (preferably as plain ASCII or Postscript).
Submissions should not exceed 10 (A4 or letter) pages, typeset in
10-12 points, with at least 2.5 cm / 1 inch margins. Submitted papers
should be anonymous and be accompanied by an e-mail listing the
following details:
- Title
- Authors' names and affiliation
- Address
- E-mail addresses
All submissions will be reviewed by an international program
committee. The accepted papers will be made available in a summer
school reader. If sufficiently many high-quality papers are
submitted, we intend to publish them in an edited volume.
Submissions should be sent before March 15, 2000 to the following
address:
Sandra Kuebler
Universitaet Tuebingen
Seminar fuer Sprachwissenschaft
Wilhelmstr. 113
D-72074 Tuebingen
Germany
kuebler at sfs.nphil.uni-tuebingen.de
If electronic submission is impossible, please send four copies of
your paper to the above address. Informal enquiries by e-mail to the
organizers are welcome.
IMPORTANT DATES:
Mar 15, 2002: Deadline for submissions
Apr 15, 2002: Notification of acceptance
May 15, 2002: Final version due
Aug 5, 2002: Start of workshop
PROGRAM COMMITTEE:
Steven Abney
Anja Belz
Rens Bod
Antal van den Bosch
Sabine Buchholz
Walter Daelemans
Ido Dagan
Herve Dejean
James Hammerton
Erhard Hinrichs, co-chair
Yuval Krymolowski
Sandra Kuebler, co-chair
Paola Merlo
John Nerbonne TBC
Miles Osborne
Erik Tjong Kim Sang
Jorn Veenstra TBC
Andreas Wagner
FURTHER INFORMATION:
To obtain further information about ESSLLI-2002 please visit
http://www.esslli2002.it/
This workshop is held as part of the ESSLLI-2002 summer
school. Therefore all workshop participants are required to register
for ESSLLI-2002. Registration information will be announced in due
time by the local organizers on the ESSLLI-2002 website.
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