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.
BibTeX
@inproceedings{pasquer-etal-2020-verbal,
title = "Verbal Multiword Expression Identification: Do We Need a Sledgehammer to Crack a Nut?",
author = "Pasquer, Caroline and
Savary, Agata and
Ramisch, Carlos and
Antoine, Jean-Yves",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.296/",
doi = "10.18653/v1/2020.coling-main.296",
pages = "3333--3345"
}