Preposition Sense Disambiguation and Representation

Hongyu Gong, Jiaqi Mu, Suma Bhat, Pramod Viswanath


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.
Anthology ID:
D18-1180
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Editors:
Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1510–1521
Language:
URL:
https://aclanthology.org/D18-1180
DOI:
10.18653/v1/D18-1180
Bibkey:
Cite (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.
Cite (Informal):
Preposition Sense Disambiguation and Representation (Gong et al., EMNLP 2018)
Copy Citation:
PDF:
https://aclanthology.org/D18-1180.pdf
Attachment:
 D18-1180.Attachment.zip
Code
 HongyuGong/PrepositionSenseDisambiguation