Abstract
This paper is concerned with building deep grammatical relation (GR) analysis using data-driven approach. To deal with this problem, we propose graph merging, a new perspective, for building flexible dependency graphs: Constructing complex graphs via constructing simple subgraphs. We discuss two key problems in this perspective: (1) how to decompose a complex graph into simple subgraphs, and (2) how to combine subgraphs into a coherent complex graph. Experiments demonstrate the effectiveness of graph merging. Our parser reaches state-of-the-art performance and is significantly better than two transition-based parsers.- Anthology ID:
- K17-1005
- Volume:
- Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)
- Month:
- August
- Year:
- 2017
- Address:
- Vancouver, Canada
- Editors:
- Roger Levy, Lucia Specia
- Venue:
- CoNLL
- SIG:
- SIGNLL
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 26–35
- Language:
- URL:
- https://aclanthology.org/K17-1005
- DOI:
- 10.18653/v1/K17-1005
- Cite (ACL):
- Weiwei Sun, Yantao Du, and Xiaojun Wan. 2017. Parsing for Grammatical Relations via Graph Merging. In Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017), pages 26–35, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Parsing for Grammatical Relations via Graph Merging (Sun et al., CoNLL 2017)
- PDF:
- https://preview.aclanthology.org/add_acl24_videos/K17-1005.pdf