There are several approaches making it possible to treat syntactic ambiguity: one can for example decide to apply disambiguation as soon as possible, in particular using statistical methods. One can on the contrary choose to delay to the maximum the disambiguation and to maintain the ambiguity, possibly until the end of the processing. These two approaches are not necessarily contradictory. However, the problem remains very difficult to solve and many of the methods proposed amount to little more than the traditional enumeration.

Factorizing the information (for example in using parse forests) can from this point of view play a very significant role and even lead to effective solutions.

The goal of this workshop is to propose an overview of the current techniques for the representation and the treatment of syntactic ambiguity in natural language processing.

Invited Speakers

• Bernard Lang -- INRIA
• David Weir -- Cognitive and Computing Science / University of Sussex

Proceedings

Syntactic Disambiguation: A Neural Network Approach
Martha Analía Alegre, Dpto. LSI, Universitat Politécnica de Catalunya
Josep Ma. Sopena, Augusti Lloveras, Lab de Neurocomputaciò, Universitat de Barcelona

Stratégies linguistique de désambiguïsation syntaxique basée sur corpus
Didier Bourigault, Cécile Fabre, Université de Toulouse Le Mirail

Dealing with ambiguities in an answer extraction system
Diego Mollà, Michael Hess, University of Zurich

Shared semantic dependency representations for LTAG
Kim Gerdes, TALANA, Université de Paris 7
Patrice Lopez, DFKI Saarbrücken

Factorisation d'arbres à l'aide d'arbres partagés
Philippe Blache, LPL-CNRS, Aix-en-Provence

Créer, extraire et manipuler des forêts partagées avec DyALog
Eric Villemonte de la Clergerie, ATOLL, INRIA

Chair

• Philippe Blache -- CNRS / Université de Provence
• Eric de la Clergerie -- INRIA