@inproceedings{yi-etal-2021-unifying,
title = "Unifying Discourse Resources with Dependency Framework",
author = "Yi, Cheng and
Sujian, Li and
Yueyuan, Li",
booktitle = "Proceedings of the 20th Chinese National Conference on Computational Linguistics",
month = aug,
year = "2021",
address = "Huhhot, China",
publisher = "Chinese Information Processing Society of China",
url = "https://aclanthology.org/2021.ccl-1.94",
pages = "1058--1065",
abstract = "{``}For text-level discourse analysis there are various discourse schemes but relatively few labeleddata because discourse research is still immature and it is labor-intensive to annotate the innerlogic of a text. In this paper we attempt to unify multiple Chinese discourse corpora under different annotation schemes with discourse dependency framework by designing semi-automatic methods to convert them into dependency structures. We also implement several benchmark dependency parsers and research on how they can leverage the unified data to improve performance.1{''}",
language = "English",
}
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<abstract>“For text-level discourse analysis there are various discourse schemes but relatively few labeleddata because discourse research is still immature and it is labor-intensive to annotate the innerlogic of a text. In this paper we attempt to unify multiple Chinese discourse corpora under different annotation schemes with discourse dependency framework by designing semi-automatic methods to convert them into dependency structures. We also implement several benchmark dependency parsers and research on how they can leverage the unified data to improve performance.1”</abstract>
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%0 Conference Proceedings
%T Unifying Discourse Resources with Dependency Framework
%A Yi, Cheng
%A Sujian, Li
%A Yueyuan, Li
%S Proceedings of the 20th Chinese National Conference on Computational Linguistics
%D 2021
%8 aug
%I Chinese Information Processing Society of China
%C Huhhot, China
%G English
%F yi-etal-2021-unifying
%X “For text-level discourse analysis there are various discourse schemes but relatively few labeleddata because discourse research is still immature and it is labor-intensive to annotate the innerlogic of a text. In this paper we attempt to unify multiple Chinese discourse corpora under different annotation schemes with discourse dependency framework by designing semi-automatic methods to convert them into dependency structures. We also implement several benchmark dependency parsers and research on how they can leverage the unified data to improve performance.1”
%U https://aclanthology.org/2021.ccl-1.94
%P 1058-1065
Markdown (Informal)
[Unifying Discourse Resources with Dependency Framework](https://aclanthology.org/2021.ccl-1.94) (Yi et al., CCL 2021)
ACL