To Be or Not To Be a Verbal Multiword Expression: A Quest for Discriminating Features
Résumé
Automatic identification of mutiword expressions (MWEs) is a prerequisite 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 classi-fier using the optimal set of only 6 features out-performs the best systems from a recent shared task on the French seen data.
Domaines
Informatique et langage [cs.CL]Origine | Fichiers produits par l'(les) auteur(s) |
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