TAL-MLNLP 2010 : TAL special issue on Machine Learning for NLP
Call For Papers
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 language acquisition.
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 OrlÃ©ans
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:
http://www.atala.org/English-style-files. 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.
- Pieter Adriaans, HSC Lab, UniversitÃ© d'Amsterdam, Pays-Bas
- Massih Amini, LIP6, Paris et ITI-CNRC, Canada
- Walter Daelemans, CNTS, UniversitÃ© d'Anvers, Belgique
- Pierre Dupont, UniversitÃ© Catholique de Louvain, Belgique
- Alexander Clark, Royal Holloway, UniversitÃ© de Londres, Grande-Bretagne
- HervÃ© Dejean, Xerox Center, Grenoble
- George Foster, ITI-CNRC, Canada
- Colin de la Higuera, Laboratoire Hubert Curien, UniversitÃ© de St Etienne
- FranÃ§ois Denis, LIF, UniversitÃ© de Marseille
- Patrick Gallinari, LIP6, UniversitÃ© de Paris 6
- Cyril Goutte, ITI-CNRC, Canada
- Laurent Miclet, Enssat, Lannion
- Richard Moot, CNRS, Bordeaux
- Emmanuel Morin, LINA, UniversitÃ© de Nantes
- Jose Oncina, PRAI Group, UniversitÃ© dâ^À^ÙAlicante, Espagne
- Pascale SÃ©billot, IRISA, INSA Rennes
- Marc Tommasi, LIFL-Inria, UniversitÃ© de Lille
- Menno van Zaanen, ILK, University of Tilburg, Pays-Bas