Preposition Sense Disambiguation and Representation
Hongyu Gong, Jiaqi Mu, Suma Bhat, Pramod Viswanath · 2018
Prepositions are highly polysemous, and their variegated senses encode significant semantic information.In this paper we match each preposition's left-and right context, and their interplay to the geometry of the word vectors to the left and right of the preposition.Extracting these features from a large corpus and using them with machine learning models makes for an efficient preposition sense disambiguation (PSD) algorithm, which is comparable to and better than state-of-the-art on two benchmark datasets.Our reliance on no linguistic tool allows us to scale the PSD algorithm to a large corpus and learn sensespecific preposition representations.The crucial abstraction of preposition senses as word representations permits their use in downstream applications-phrasal verb paraphrasing and preposition selection-with new state-ofthe-art results.