@inproceedings{shi-lee-2021-transition,
title = "Transition-based Bubble Parsing: Improvements on Coordination Structure Prediction",
author = "Shi, Tianze and
Lee, Lillian",
editor = "Zong, Chengqing and
Xia, Fei and
Li, Wenjie and
Navigli, Roberto",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.557",
doi = "10.18653/v1/2021.acl-long.557",
pages = "7167--7182",
abstract = "We propose a transition-based bubble parser to perform coordination structure identification and dependency-based syntactic analysis simultaneously. Bubble representations were proposed in the formal linguistics literature decades ago; they enhance dependency trees by encoding coordination boundaries and internal relationships within coordination structures explicitly. In this paper, we introduce a transition system and neural models for parsing these bubble-enhanced structures. Experimental results on the English Penn Treebank and the English GENIA corpus show that our parsers beat previous state-of-the-art approaches on the task of coordination structure prediction, especially for the subset of sentences with complex coordination structures.",
}
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%0 Conference Proceedings
%T Transition-based Bubble Parsing: Improvements on Coordination Structure Prediction
%A Shi, Tianze
%A Lee, Lillian
%Y Zong, Chengqing
%Y Xia, Fei
%Y Li, Wenjie
%Y Navigli, Roberto
%S Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F shi-lee-2021-transition
%X We propose a transition-based bubble parser to perform coordination structure identification and dependency-based syntactic analysis simultaneously. Bubble representations were proposed in the formal linguistics literature decades ago; they enhance dependency trees by encoding coordination boundaries and internal relationships within coordination structures explicitly. In this paper, we introduce a transition system and neural models for parsing these bubble-enhanced structures. Experimental results on the English Penn Treebank and the English GENIA corpus show that our parsers beat previous state-of-the-art approaches on the task of coordination structure prediction, especially for the subset of sentences with complex coordination structures.
%R 10.18653/v1/2021.acl-long.557
%U https://aclanthology.org/2021.acl-long.557
%U https://doi.org/10.18653/v1/2021.acl-long.557
%P 7167-7182
Markdown (Informal)
[Transition-based Bubble Parsing: Improvements on Coordination Structure Prediction](https://aclanthology.org/2021.acl-long.557) (Shi & Lee, ACL-IJCNLP 2021)
ACL