Semi Supervised Preposition-Sense Disambiguation using Multilingual Data

Hila Gonen, Yoav Goldberg


Abstract
Prepositions are very common and very ambiguous, and understanding their sense is critical for understanding the meaning of the sentence. Supervised corpora for the preposition-sense disambiguation task are small, suggesting a semi-supervised approach to the task. We show that signals from unannotated multilingual data can be used to improve supervised preposition-sense disambiguation. Our approach pre-trains an LSTM encoder for predicting the translation of a preposition, and then incorporates the pre-trained encoder as a component in a supervised classification system, and fine-tunes it for the task. The multilingual signals consistently improve results on two preposition-sense datasets.
Anthology ID:
C16-1256
Volume:
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
Month:
December
Year:
2016
Address:
Osaka, Japan
Venue:
COLING
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
2718–2729
Language:
URL:
https://www.aclweb.org/anthology/C16-1256
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/C16-1256.pdf