Abstract
We present a transition-based dependency parser that uses a convolutional neural network to compose word representations from characters. The character composition model shows great improvement over the word-lookup model, especially for parsing agglutinative languages. These improvements are even better than using pre-trained word embeddings from extra data. On the SPMRL data sets, our system outperforms the previous best greedy parser (Ballesteros et. al, 2015) by a margin of 3% on average.- Anthology ID:
- P17-2106
- Volume:
- Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
- Month:
- July
- Year:
- 2017
- Address:
- Vancouver, Canada
- Editors:
- Regina Barzilay, Min-Yen Kan
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 672–678
- Language:
- URL:
- https://aclanthology.org/P17-2106
- DOI:
- 10.18653/v1/P17-2106
- Cite (ACL):
- Xiang Yu and Ngoc Thang Vu. 2017. Character Composition Model with Convolutional Neural Networks for Dependency Parsing on Morphologically Rich Languages. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 672–678, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Character Composition Model with Convolutional Neural Networks for Dependency Parsing on Morphologically Rich Languages (Yu & Vu, ACL 2017)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/P17-2106.pdf