@inproceedings{tai-etal-2021-yi,
title = "汉语语体特征的计量与分类研究(A study on the measurement and classification of {C}hinese stylistic features)",
author = "Tai, Qinqing and
Rao, Gaoqi",
editor = "Li, Sheng and
Sun, Maosong and
Liu, Yang and
Wu, Hua and
Liu, Kang and
Che, Wanxiang and
He, Shizhu and
Rao, Gaoqi",
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-1.37",
pages = "398--412",
abstract = "本文运用语料库和统计方法对汉语语体进行特征的计量研究,并进一步实现自动分类任务。首先通过单因素方差分析描述语体特征区别不同语体的作用和功能。其次,选取其中具有区分度的语言要素拟合逻辑回归模型,量化语体表达形式并观察特征对语体构成的重要性,并通过聚类计算得到了语体的范畴分类体系。最后,以具有代表性的机器学习模型为分类器,挖掘不同组合特征的结构对于语体自动分类的影响。得出在{``}词2n+词类2n+标点符号2n+语言特征{''}的组合特征上,取得了最好的分类结果,随机森林模型达到97.25{\%}的准确率。",
language = "Chinese",
}
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<abstract>本文运用语料库和统计方法对汉语语体进行特征的计量研究,并进一步实现自动分类任务。首先通过单因素方差分析描述语体特征区别不同语体的作用和功能。其次,选取其中具有区分度的语言要素拟合逻辑回归模型,量化语体表达形式并观察特征对语体构成的重要性,并通过聚类计算得到了语体的范畴分类体系。最后,以具有代表性的机器学习模型为分类器,挖掘不同组合特征的结构对于语体自动分类的影响。得出在“词2n+词类2n+标点符号2n+语言特征”的组合特征上,取得了最好的分类结果,随机森林模型达到97.25%的准确率。</abstract>
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%0 Conference Proceedings
%T 汉语语体特征的计量与分类研究(A study on the measurement and classification of Chinese stylistic features)
%A Tai, Qinqing
%A Rao, Gaoqi
%Y Li, Sheng
%Y Sun, Maosong
%Y Liu, Yang
%Y Wu, Hua
%Y Liu, Kang
%Y Che, Wanxiang
%Y He, Shizhu
%Y Rao, Gaoqi
%S Proceedings of the 20th Chinese National Conference on Computational Linguistics
%D 2021
%8 August
%I Chinese Information Processing Society of China
%C Huhhot, China
%G Chinese
%F tai-etal-2021-yi
%X 本文运用语料库和统计方法对汉语语体进行特征的计量研究,并进一步实现自动分类任务。首先通过单因素方差分析描述语体特征区别不同语体的作用和功能。其次,选取其中具有区分度的语言要素拟合逻辑回归模型,量化语体表达形式并观察特征对语体构成的重要性,并通过聚类计算得到了语体的范畴分类体系。最后,以具有代表性的机器学习模型为分类器,挖掘不同组合特征的结构对于语体自动分类的影响。得出在“词2n+词类2n+标点符号2n+语言特征”的组合特征上,取得了最好的分类结果,随机森林模型达到97.25%的准确率。
%U https://aclanthology.org/2021.ccl-1.37
%P 398-412
Markdown (Informal)
[汉语语体特征的计量与分类研究(A study on the measurement and classification of Chinese stylistic features)](https://aclanthology.org/2021.ccl-1.37) (Tai & Rao, CCL 2021)
ACL