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:
- 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)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/C16-1256.pdf