Choosing features for classifying multiword expressions

TL;DR

This paper discusses the importance of feature selection in classifying multiword expressions and proposes an enhanced classification method that considers multilingual features for better computational utility.

Contribution

It introduces an improved classification approach for MWEs that accounts for feature reliability and multilingual considerations.

Findings

Certain features are more reliable for classifying MWEs.

Multilingual features improve classification robustness.

Enhanced classification benefits computational applications.

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.