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.This work is licensed under a Creative Commons Attribution 4.0 International License. License details: http:// creativecommons.org/licenses/by/4.0/.1 Henceforth, the lexicalized components of a MWE, i.e. those always realized by the same lexemes, appear in bold.2 Henceforth, literal and coincidental occurrences are highlighted with wavy underlining, following Savary et al. (2019b).