A Clause-Level Hybrid Approach to Chinese Empty Element Recovery
Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence. Pages 2113-2118.
Abstract
Empty elements (EEs) play a critical role in Chinese syntactic, semantic and discourse analysis. Previous studies employ a language-independent sentence-level approach to EE recovery, by casting it as a linear tagging or structured parsing problem. In comparison, this paper proposes a clause-level hybrid approach to address specific problems in Chinese EE recovery, which recovers EEs in Chinese language from the clause perspective and integrates the advantages of both linear tagging and structured parsing. In particular, a comma disambiguation method is employed to improve syntactic parsing and help determine clauses in Chinese. In this way, the noise introduced by sentence-level syntactic parsing and multiple EEs in the same position of a linear sentence can be well addressed. Evaluation on Chinese Treebank 6.0 shows the significant performance improvement of our clause-level hybrid approach over the state-of-the-art sentence-level baselines, and its great impact on a state-of-the-art Chinese syntactic parser.
@inproceedings{ijcai2013p312,
title = {A Clause-Level Hybrid Approach to Chinese Empty Element Recovery},
author = {Kong, Fang and Zhou, Guodong},
booktitle = {Proceedings of the Twenty-Third International Joint Conference on
Artificial Intelligence, {IJCAI-13}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Francesca Rossi},
pages = {2113--2118},
year = {2013},
month = {8},
url = {https://ijcai.org/proceedings/2013/312},
}
TY - CPAPER TI - A Clause-Level Hybrid Approach to Chinese Empty Element Recovery AU - Kong, Fang AU - Zhou, Guodong T2 - Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI-13 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2013 SP - 2113 EP - 2118 UR - https://ijcai.org/proceedings/2013/312 ER -Copied
Machine-readable versions of this paper, for tools and AI agents: