Abstract
We present a comparative study of transition-, graph- and PCFG-based models aimed at illuminating more precisely the likely contribution of CFGs in improving Chinese dependency parsing accuracy, especially by combining heterogeneous models. Inspired by the impact of a constituency grammar on dependency parsing, we propose several strategies to acquire pseudo CFGs only from dependency annotations. Compared to linguistic grammars learned from rich phrase-structure treebanks, well designed pseudo grammars achieve similar parsing accuracy and have equivalent contributions to parser ensemble. Moreover, pseudo grammars increase the diversity of base models; therefore, together with all other models, further improve system combination. Based on automatic POS tagging, our final model achieves a UAS of 87.23%, resulting in a significant improvement of the state of the art.- Anthology ID:
- Q13-1025
- Volume:
- Transactions of the Association for Computational Linguistics, Volume 1
- Month:
- Year:
- 2013
- Address:
- Cambridge, MA
- Editors:
- Dekang Lin, Michael Collins
- Venue:
- TACL
- SIG:
- Publisher:
- MIT Press
- Note:
- Pages:
- 301–314
- Language:
- URL:
- https://preview.aclanthology.org/build-pipeline-with-new-library/Q13-1025/
- DOI:
- 10.1162/tacl_a_00229
- Cite (ACL):
- Weiwei Sun and Xiaojun Wan. 2013. Data-driven, PCFG-based and Pseudo-PCFG-based Models for Chinese Dependency Parsing. Transactions of the Association for Computational Linguistics, 1:301–314.
- Cite (Informal):
- Data-driven, PCFG-based and Pseudo-PCFG-based Models for Chinese Dependency Parsing (Sun & Wan, TACL 2013)
- PDF:
- https://preview.aclanthology.org/build-pipeline-with-new-library/Q13-1025.pdf