  Automatic identification of mutiword expressions (MWEs) is a pre-requisite
for semantically-oriented downstream applications. This task is challenging
because MWEs, especially verbal ones (VMWEs), exhibit surface variability.
However, this variability is usually more restricted than in regular (non-VMWE)
constructions, which leads to various variability profiles. We use this fact to
determine the optimal set of features which could be used in a supervised
classification setting to solve a subproblem of VMWE identification: the
identification of occurrences of previously seen VMWEs. Surprisingly, a simple
custom frequency-based feature selection method proves more efficient than
other standard methods such as Chi-squared test, information gain or decision
trees. An SVM classifier using the optimal set of only 6 features outperforms
the best systems from a recent shared task on the French seen data.
