Abstract
We model a dependency graph as a book, a particular kind of topological space, for semantic dependency parsing. The spine of the book is made up of a sequence of words, and each page contains a subset of noncrossing arcs. To build a semantic graph for a given sentence, we design new Maximum Subgraph algorithms to generate noncrossing graphs on each page, and a Lagrangian Relaxation-based algorithm tocombine pages into a book. Experiments demonstrate the effectiveness of the bookembedding framework across a wide range of conditions. Our parser obtains comparable results with a state-of-the-art transition-based parser.- Anthology ID:
- P17-1077
- Volume:
- Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2017
- Address:
- Vancouver, Canada
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 828–838
- Language:
- URL:
- https://aclanthology.org/P17-1077
- DOI:
- 10.18653/v1/P17-1077
- Cite (ACL):
- Weiwei Sun, Junjie Cao, and Xiaojun Wan. 2017. Semantic Dependency Parsing via Book Embedding. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 828–838, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Semantic Dependency Parsing via Book Embedding (Sun et al., ACL 2017)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/P17-1077.pdf