Modelling Polysemy in Adjective Classes by Multi-Label Classification
Gemma Boleda, Sabine Schulte im Walde, Toni Badía · Empirical Methods in Natural Language Processing · 2007
This paper assesses the role of multi-label classification in modelling polysemy for language acquisition tasks. We focus on the acquisition of semantic classes for Catalan adjectives, and show that polysemy acquisition naturally suits architectures used for multilabel classification. Furthermore, we explore the performance of information drawn from different levels of linguistic description, using feature sets based on morphology, syntax, semantics, and n-gram distribution. Finally, we demonstrate that ensemble classifiers are a powerful and adequate way to combine different types of linguistic evidence: a simple, majority voting ensemble classifier improves the accuracy from 62.5% (best single classifier) to 84%.