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The PARSEME Shared Task on Automatic Identification of Verbal Multiword Expressions

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.
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Contributor : Agata Savary <>
Submitted on : Monday, April 10, 2017 - 2:00:23 PM
Last modification on : Monday, January 4, 2021 - 3:05:31 PM


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  • HAL Id : hal-01504624, version 1


Agata Savary, Carlos Ramisch, Silvio Cordeiro, Federico Sangati, Veronika Vincze, et al.. The PARSEME Shared Task on Automatic Identification of Verbal Multiword Expressions. MWE 2017 - Proceedings of the 13th Workshop on Multiword Expressions, Apr 2017, Valencia, Spain. pp.31 - 47. ⟨hal-01504624⟩



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