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1 BDTLN - Bases de données et traitement des langues naturelles LI - Laboratoire d-Informatique de l-Université de Tours 2 LIF - Laboratoire d-informatique Fondamentale de Marseille 3 Chercheur Indépendant 4 University of Szeged Szeged 5 University of Dusseldorf 6 ALPAGE - Analyse Linguistique Profonde à Grande Echelle ; Large-scale deep linguistic processing UPD7 - Université Paris Diderot - Paris 7, Inria de Paris 7 Uppsala University 8 ILSP - Institute for Language and Speech Processing 9 Bulgarian Academy of Sciences 10 ULR - Université de La Rochelle

Abstract : Multiword expressions MWEs are known as a -pain in the neck- for NLP due to their idiosyncratic behaviour. While some categories of MWEs have been addressed by many studies, verbal MWEs VMWEs, such as to take a decision, to break one-s heart or to turn off, have been rarely modelled. This is notably due to their syntactic variability, which hinders treating them as - words with spaces -. We describe an initiative meant to bring about substantial progress in understanding, modelling and processing VMWEs. It is a joint effort, carried out within a European research network, to elaborate universal terminologies and annotation guidelines for 18 languages. Its main outcome is a multilingual 5-million-word annotated corpus which underlies a shared task on automatic identification of VMWEs. This paper presents the corpus annotation methodology and outcome, the shared task organisation and the results of the participating systems.

Keywords : annotation multiword expressions identification multilingualism

Autor: Agata Savary - Carlos Ramisch - Silvio Cordeiro - Federico Sangati - Veronika Vincze - Behrang Qasemizadeh - Marie Candito - Fabi



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