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Preposition Sense Disambiguation and Representation

2018-10-01EMNLP 2018Code Available0· sign in to hype

Hongyu Gong, Jiaqi Mu, Suma Bhat, Pramod Viswanath

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Abstract

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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