Contributive resources, such as wikipedia, have proved to be valuable in Natural Language Processing or Multilingual Information Retrieval applications.This article focusses on Wiktionary, the dictionary part of the collaborative resources sponsored by the Wikimedia foundation. In this article we present a word net that has been extracted from French, English and German wiktionaries. We present the structure of this word net and discuss the specific extraction problems induced by this kind of contributive resources and the method used to overcome them. Then we show how we represent the extracted data as a Lexical Markup Framework (LMF) compatible lexical network represented in Resource Description Framework (RDF) format.

Cet article présente l’utilisation de « Jibiki » (la plateforme de développement du serveur Web Papillon) dans le cadre du projet LexALP1. Le but de ce projet est d’harmoniser la terminologie des quatre langues (français, allemand, italien et slovène) de la Convention Alpine2 de sorte que les états membres puissent coopérer efficacement. Pour cela, le projet utilise la plateforme Jibiki afin de construire une banque terminologique permettant de comparer la terminologie spécialisée de sept systèmes légaux dans quatre langues, et de l’harmoniser, optimisant ainsi la compréhension entre les états alpins sur des questions environnementales au niveau supranational. Dans cet article, nous présentons comment peut être employée la plateforme générique Jibiki afin de gérer un dictionnaire particulier.

We present the architecture of the UNL-French deconverter, which "generates" from the UNL interlingua by first "localizing" the UNL form for French, within UNL, and then applying slightly adapted but classical transfer and generation techniques, implemented in GETA's Ariane-G5 environment, supplemented by some UNL-specific tools. Online interaction can be used during deconversion to enhance output quality and is now used for development purposes. We show how interaction could be delayed and embedded in the postedition phase, which would then interact not directly with the output text, but indirectly with several components of the deconverter. Interacting online or offline can improve the quality not only of the utterance at hand, but also of the utterances processed later, as various preferences may be automatically changed to let the deconverter "learn".