Disambiguating prepositional phrase attachment sites with sense information captured in contextualized distributional data

Abstract

This work presents a supervised prepositional phrase (PP) attachment disambiguation system that uses contextualized distributional information as the distance metric for a nearest-neighbor classifier. Contextualized word vectors constructed from the GigaWord Corpus provide a method for implicit Word Sense Disambiguation (WSD), whose reliability helps this system outperform baselines and achieve comparable results to those of systems with full WSD modules. This suggests that targeted WSD methods are preferable to ignoring sense information and also to implementing WSD as an independent module in a pipeline.