@inproceedings{fan-etal-2020-rong,
title = "融合全局和局部信息的汉语宏观篇章结构识别(Combining Global and Local Information to Recognize {C}hinese Macro Discourse Structure)",
author = "Fan, Yaxin and
Jiang, Feng and
Chu, Xiaomin and
Li, Peifeng and
Zhu, Qiaoming",
editor = "Sun, Maosong and
Li, Sujian and
Zhang, Yue and
Liu, Yang",
booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
month = oct,
year = "2020",
address = "Haikou, China",
publisher = "Chinese Information Processing Society of China",
url = "https://aclanthology.org/2020.ccl-1.18/",
pages = "183--194",
language = "zho",
abstract = "作为宏观篇章分析中的基础任务,篇章结构识别任务的目的是识别相邻篇章单元之间的结构,并层次化构建篇章结构树。已有的工作只考虑局部的结构和语义信息或只考虑全局信息。因此,本文提出了一种融合全局和局部信息的指针网络模型,该模型在考虑全局的语义信息同时,又考虑局部段落间的语义关系密切程度,从而有效地提高宏观篇章结构识别的能力。在汉语宏观篇章树库(MCDTB)的实验结果表明,本文所提出的模型性能优于目前性能最好的模型。"
}
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<abstract>作为宏观篇章分析中的基础任务,篇章结构识别任务的目的是识别相邻篇章单元之间的结构,并层次化构建篇章结构树。已有的工作只考虑局部的结构和语义信息或只考虑全局信息。因此,本文提出了一种融合全局和局部信息的指针网络模型,该模型在考虑全局的语义信息同时,又考虑局部段落间的语义关系密切程度,从而有效地提高宏观篇章结构识别的能力。在汉语宏观篇章树库(MCDTB)的实验结果表明,本文所提出的模型性能优于目前性能最好的模型。</abstract>
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%0 Conference Proceedings
%T 融合全局和局部信息的汉语宏观篇章结构识别(Combining Global and Local Information to Recognize Chinese Macro Discourse Structure)
%A Fan, Yaxin
%A Jiang, Feng
%A Chu, Xiaomin
%A Li, Peifeng
%A Zhu, Qiaoming
%Y Sun, Maosong
%Y Li, Sujian
%Y Zhang, Yue
%Y Liu, Yang
%S Proceedings of the 19th Chinese National Conference on Computational Linguistics
%D 2020
%8 October
%I Chinese Information Processing Society of China
%C Haikou, China
%G zho
%F fan-etal-2020-rong
%X 作为宏观篇章分析中的基础任务,篇章结构识别任务的目的是识别相邻篇章单元之间的结构,并层次化构建篇章结构树。已有的工作只考虑局部的结构和语义信息或只考虑全局信息。因此,本文提出了一种融合全局和局部信息的指针网络模型,该模型在考虑全局的语义信息同时,又考虑局部段落间的语义关系密切程度,从而有效地提高宏观篇章结构识别的能力。在汉语宏观篇章树库(MCDTB)的实验结果表明,本文所提出的模型性能优于目前性能最好的模型。
%U https://aclanthology.org/2020.ccl-1.18/
%P 183-194
Markdown (Informal)
[融合全局和局部信息的汉语宏观篇章结构识别(Combining Global and Local Information to Recognize Chinese Macro Discourse Structure)](https://aclanthology.org/2020.ccl-1.18/) (Fan et al., CCL 2020)
ACL