@inproceedings{gong-etal-2018-preposition,
title = "Preposition Sense Disambiguation and Representation",
author = "Gong, Hongyu and
Mu, Jiaqi and
Bhat, Suma and
Viswanath, Pramod",
editor = "Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1180",
doi = "10.18653/v1/D18-1180",
pages = "1510--1521",
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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<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.</abstract>
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%0 Conference Proceedings
%T Preposition Sense Disambiguation and Representation
%A Gong, Hongyu
%A Mu, Jiaqi
%A Bhat, Suma
%A Viswanath, Pramod
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F gong-etal-2018-preposition
%X 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.
%R 10.18653/v1/D18-1180
%U https://aclanthology.org/D18-1180
%U https://doi.org/10.18653/v1/D18-1180
%P 1510-1521
Markdown (Informal)
[Preposition Sense Disambiguation and Representation](https://aclanthology.org/D18-1180) (Gong et al., EMNLP 2018)
ACL
- Hongyu Gong, Jiaqi Mu, Suma Bhat, and Pramod Viswanath. 2018. Preposition Sense Disambiguation and Representation. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1510–1521, Brussels, Belgium. Association for Computational Linguistics.