Verbal Multiword Expression Identification: Do We Need a Sledgehammer to Crack a Nut?

Caroline Pasquer, Agata Savary, Carlos Ramisch, Jean-Yves Antoine

Abstract
Automatic identification of multiword expressions (MWEs), like ‘to cut corners’ (to do an incomplete job), is a pre-requisite for semantically-oriented downstream applications. This task is challenging because MWEs, especially verbal ones (VMWEs), exhibit surface variability. This paper deals with a subproblem of VMWE identification: the identification of occurrences of previously seen VMWEs. A simple language-independent system based on a combination of filters competes with the best systems from a recent shared task: it obtains the best averaged F-score over 11 languages (0.6653) and even the best score for both seen and unseen VMWEs due to the high proportion of seen VMWEs in texts. This highlights the fact that focusing on the identification of seen VMWEs could be a strategy to improve VMWE identification in general.
Anthology ID:
2020.coling-main.296
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Editors:
Donia Scott, Nuria Bel, Chengqing Zong
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
3333–3345
Language:
URL:
https://aclanthology.org/2020.coling-main.296
DOI:
10.18653/v1/2020.coling-main.296
Bibkey:
Cite (ACL):
Caroline Pasquer, Agata Savary, Carlos Ramisch, and Jean-Yves Antoine. 2020. Verbal Multiword Expression Identification: Do We Need a Sledgehammer to Crack a Nut?. In Proceedings of the 28th International Conference on Computational Linguistics, pages 3333–3345, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
Verbal Multiword Expression Identification: Do We Need a Sledgehammer to Crack a Nut? (Pasquer et al., COLING 2020)
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PDF:
https://preview.aclanthology.org/nschneid-patch-4/2020.coling-main.296.pdf
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