Combine Constituent and Dependency Parsing via Reranking
Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence. Pages 2155-2160.
Abstract
This paper presents a reranking approach to combining constituent and dependency parsing, aimed at improving parsing performance on both sides. Most previous combination methods rely on complicated joint decoding to integrate graph- and transition-based dependency models. Instead, our approach makes use of a high-performance probabilistic context free grammar (PCFG) model to output k-best candidate constituent trees, and then a dependency parsing model to rerank the trees by their scores from both models, so as to get the most probable parse. Experimental results show that this reranking approach achieves the highest accuracy of constituent and dependency parsing on Chinese treebank (CTB5.1) and a comparable performance to the state of the art on English treebank (WSJ).
@inproceedings{ijcai2013p318,
title = {Combine Constituent and Dependency Parsing via Reranking},
author = {Ren, Xiaona and Chen, Xiao and Kit, Chunyu},
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 = {2155--2160},
year = {2013},
month = {8},
url = {https://ijcai.org/proceedings/2013/318},
}
TY - CPAPER TI - Combine Constituent and Dependency Parsing via Reranking AU - Ren, Xiaona AU - Chen, Xiao AU - Kit, Chunyu 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 - 2155 EP - 2160 UR - https://ijcai.org/proceedings/2013/318 ER -Copied
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