[Elsnet-list] Extended Deadline CFP: TAL issue on "Machine Learning
Eric De la clergerie
Eric.De_La_Clergerie at inria.fr
Tue Jun 2 20:39:21 CEST 2009
To satisfy some requests, the deadline for papers for the special
issue of the review TAL about "Machine Learning for NLP" is postponed :
- 1st july : detailed summary
- ** 6th july ** : paper
Mark Steedman, Isabelle Tellier,
Machine Learning for NLP
The revue TAL (http://www.atala.org/-Revue-TAL) proposes a call for
papers on the subject of "Machine Learning for NLP". Machine Learning
is the study of algorithms that allow computer programs to
automatically improve through experience (definition proposed by Tom
Mitchell in his "Machine Learning" book). This domain has drastically
increased in the last few years, and its interactions with NLP are
more and more tight and frequent.
From a linguistic point of view, the interests for this evolution are
numerous. As a matter of fact, manually built resources are
time-consuming and expensive, and the process must be started again
for each distinct language and each distinct sub-domain of a language.
Machine Learning offers an attractive alternative, allowing to obtain
or improve at a lower cost such a resource, with better guaranties of
robustness and coverage. The inductive approach, used for a long time
in the "corpus linguistic" community, can now be operationalized at a
large scale, and its results be rigorously tested. And formal theories
of learning also contribute to the long-standing debate about natural
From a Machine Learning point of view, NLP is a rich application
domain where problems are numerous and difficult, and for which many
data are usually available. But the interpretability of the obtained
results is often problematic. More and more subtle specialist-reserved
mathematical device are used : in this context, is linguistics still
useful ? What confidence can a linguist have on the result of a
Machine Learning system ?
A number of the electronic review TAL will be dedicated to this theme.
Beyond reports about yet another experiment applying a special Machine
Learning method on a special linguistic task, more general theoretical
and methodological reflexions are encouraged. For each contribution
and each method used, a special effort should be made to clarify what
are the linguistic as well as computational underlying hypotheses.
The Machine Learning approach considered can be : - either
theoretical, concerning learnability/non learnability results for
classes of objects, with respect to formal criteria - either
empirical, based on an experimental protocol exploiting annotated (in
the case of supervised learning) or not annotated (in the case of non
supervised learning) data
The methods used can be :
- symbolic (grammatical inference, ILP...)
- based on probabilistic (either generative or discriminative) models
- based on similarities (neighboring, analogy, memory-based
Application domains can be :
- acquisition or improving of resources (including automata, grammars,
sub-categorisation frames, concept-based ontologies...)
- speech analysis
- corpus labeling (either lexical, syntactic, functional, thematic,
- clustering and classification of texts (according to various
possible criteria : author, content, opinion...)
- information extraction (including : extraction and typing of named
- question/answering systems
- automatic summary
- automatic translation
editors in chief :
Isabelle Tellier, LIFO/University of Orléans
Mark Steedman, ICCS/University of Edinburgh, Scotland
Contributions (25 pages maximum, PDF format) must be sent by e-mail to
the following address: (isabelle dot tellier at univ dash orleans dot
fr) Style sheets are available at the following address:
Language: manuscripts may be submitted in English or French.
French-speaking authors are requested to submit in French.
- 01/07/2009 Detailed summary (1p)
- 06/07/2009 Deadline for submission.
- 04/09/2009 Notification to authors.
- 02/10/2009 Deadline for submission of a revised version.
- 10/11/2009 Final decision.
- February 2010 publication on line.
Scientific commitee :
- Pieter Adriaans, HSC Lab, Université d'Amsterdam, Pays-Bas
- Massih Amini, LIP6, Paris et ITI-CNRC, Canada
- Walter Daelemans, CNTS, Université d'Anvers, Belgique
- Pierre Dupont, Université Catholique de Louvain, Belgique
- Alexander Clark, Royal Holloway, Université de Londres, Grande-Bretagne
- Hervé Dejean, Xerox Center, Grenoble
- George Foster, ITI-CNRC, Canada
- Colin de la Higuera, Laboratoire Hubert Curien, Université de St Etienne
- François Denis, LIF, Université de Marseille
- Patrick Gallinari, LIP6, Université de Paris 6
- Cyril Goutte, ITI-CNRC, Canada
- Laurent Miclet, Enssat, Lannion
- Richard Moot, CNRS, Bordeaux
- Emmanuel Morin, LINA, Université de Nantes
- Jose Oncina, PRAI Group, Université d’Alicante, Espagne
- Pascale Sébillot, IRISA, INSA Rennes
- Marc Tommasi, LIFL-Inria, Université de Lille
- Menno van Zaanen, ILK, University of Tilburg, Pays-Bas
Eric.De_La_Clergerie at inria.fr Projet Alpage - INRIA Rocquencourt
WWW Home Page: http://alpage.inria.fr/~clerger
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