Semi Supervised Preposition-Sense Disambiguation using Multilingual Data

Hila Gonen, Yoav Goldberg

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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
Editors:
Yuji Matsumoto, Rashmi Prasad
Venue:
COLING
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
2718–2729
Language:
URL:
https://aclanthology.org/C16-1256
DOI:
Bibkey:
Cite (ACL):
Hila Gonen and Yoav Goldberg. 2016. Semi Supervised Preposition-Sense Disambiguation using Multilingual Data. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 2718–2729, Osaka, Japan. The COLING 2016 Organizing Committee.
Cite (Informal):
Semi Supervised Preposition-Sense Disambiguation using Multilingual Data (Gonen & Goldberg, COLING 2016)
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PDF:
https://preview.aclanthology.org/teach-a-man-to-fish/C16-1256.pdf