A Neural Graph-based Approach to Verbal MWE Identification

• Jakub Waszczuk ^[1] ; Rafael Ehren ^[1] ; Regina Stodden ^[1] ; Laura Kallmeyer ^[1]
  1. [1] Heinrich Heine University Düsseldorf

     Heinrich Heine University Düsseldorf

     Kreisfreie Stadt Düsseldorf, Alemania

     
• Localización: Joint Workshop on Multiword Expressions and WordNet (MWE-WN 2019): August 2, 2019 Florence, Italy: Proceedings of the Workshop / Agata Savary (ed. lit.), Carla Parra Escartín (ed. lit.), Francis Bond (ed. lit.), Jelena Mitrovic (ed. lit.), Verginica Barbu Mititelu (ed. lit.), 2019, ISBN 978-1-950737-26-0, págs. 114-124
• Idioma: inglés
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• Resumen

  • We propose to tackle the problem of verbal multiword expression (VMWE) identification using a neural graph parsing-based approach. Our solution involves encoding VMWE annotations as labellings of dependency trees and, subsequently, applying a neural network to model the probabilities of different labellings. This strategy can be particularly effective when applied to discontinuous VMWEs and, thanks to dense, pretrained word vector representations, VMWEs unseen during training. Evaluation of our approach on three PARSEME datasets (German, French, and Polish) shows that it allows to achieve performance on par with the previous state-of- the-art (Al Saied et al., 2018).