Papers › Preposition Sense Disambiguation and Representation
Preposition Sense Disambiguation and Representation
Hongyu Gong, Jiaqi Mu, Suma Bhat, Pramod Viswanath
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 sense-specific 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-of-the-art results.
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