Paper: Modelling Polysemy in Adjective Classes by Multi-Label Classification

ACL ID D07-1018
Title Modelling Polysemy in Adjective Classes by Multi-Label Classification
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2007
Authors

This paper assesses the role of multi-label classification in modelling polysemy for lan- guage acquisition tasks. We focus on the ac- quisition of semantic classes for Catalan ad- jectives, and show that polysemy acquisition naturally suits architectures used for multi- label classification. Furthermore, we ex- plore the performance of information drawn from different levels of linguistic descrip- tion, using feature sets based on morphol- ogy, syntax, semantics, and n-gram distribu- tion. Finally, we demonstrate that ensemble classifiers are a powerful and adequate way to combine different types of linguistic ev- idence: a simple, majority voting ensemble classifier improves the accuracy from 62.5% (best single classifier) to 84%.