Choosing features for classifying multiword expressions
For computational linguists working on MWE processing, this work provides a more robust classification method, though it is incremental in nature.
The paper addresses the need for classifying multiword expressions (MWEs) by selecting features that improve classification reliability and cross-linguistic applicability, resulting in an enhanced classification framework.
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.