cs.CLMay 12, 2026

Choosing features for classifying multiword expressions

Authors: Eric Laporte

Organizations: Université Paris-Est, Laboratoire d’informatique Gaspard-Monge CNRS UMR 8049, F77454 Marne-la-Vallée, France

Abstract

Multiword expressions (MWEs) are a heterogeneous set with a glaring need for classifications. Designing a satisfactory classification involves choosing features. In the case of MWEs, many features are a priori available. Not all features are equal in terms of how reliably MWEs can be assigned to classes. Accordingly, resulting classifications may be more or less fruitful for computational use. I outline an enhanced classification. In order to increase its suitability for many languages, I use previous works taking into account various languages.

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