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Preposition Sense Disambiguation and Representation
2018
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
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
doi:10.18653/v1/d18-1180
dblp:conf/emnlp/GongMBV18
fatcat:p7q4dmh6efaw5jvcesqs4juzr4