HC-Search for Incremental Parsing
Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. Pages 2887-2892.
Abstract
Standard incremental parsing algorithm employs a single scoring function and beam-search to find the best parse tree from an exponentially large search space. Inspired by recently proposed HC-search framework, we decompose the incremental parsing algorithm into two steps: first searching a set of high-quality outputs with beam-search, and second selecting the best output with a ranking model. We learn our incremental parsing model with a relaxed learning objective. We incorporate arbitrary features in our ranking model and learn the model from fine grain ranking examples. Experimental results on standard English and Chinese datasets show our method significantly outperforms a strong baseline.
@inproceedings{ijcai2016p410,
title = {HC-Search for Incremental Parsing},
author = {Liu, Yijia and Che, Wanxiang and Qin, Bing and Liu, Ting},
booktitle = {Proceedings of the Twenty-Fifth International Joint Conference on
Artificial Intelligence, {IJCAI-16}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Subbarao Kambhampati},
pages = {2887--2892},
year = {2016},
month = {7},
url = {https://ijcai.org/proceedings/2016/410},
}
TY - CPAPER TI - HC-Search for Incremental Parsing AU - Liu, Yijia AU - Che, Wanxiang AU - Qin, Bing AU - Liu, Ting T2 - Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-16 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2016 SP - 2887 EP - 2892 UR - https://ijcai.org/proceedings/2016/410 ER -Copied
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