@inproceedings{hu-etal-2022-ji,
title = "基于特征融合的汉语被动句自动识别研究(Automatic Recognition of {C}hinese Passive Sentences Based on Feature Fusion)",
author = "Hu, Kang and
Qu, Weiguang and
Wei, Tingxin and
Zhou, Junsheng and
Gu, Yanhui and
Li, Bin",
booktitle = "Proceedings of the 21st Chinese National Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Nanchang, China",
publisher = "Chinese Information Processing Society of China",
url = "https://aclanthology.org/2022.ccl-1.35",
pages = "384--394",
abstract = "{``}汉语中的被动句根据有无被动标记词可分为有标记被动句和无标记被动句。由于其形态构成复杂多样,给自然语言理解带来很大困难,因此实现汉语被动句的自动识别对自然语言处理下游任务具有重要意义。本文构建了一个被动句语料库,提出了一个融合词性和动词论元框架信息的PC-BERT-CNN模型,对汉语被动句进行自动识别。实验结果表明,本文提出的模型能够准确地识别汉语被动句,其中有标记被动句识别F1值达到98.77{\%},无标记被动句识别F1值达到96.72{\%}。{''}",
language = "Chinese",
}
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<title>基于特征融合的汉语被动句自动识别研究(Automatic Recognition of Chinese Passive Sentences Based on Feature Fusion)</title>
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<abstract>“汉语中的被动句根据有无被动标记词可分为有标记被动句和无标记被动句。由于其形态构成复杂多样,给自然语言理解带来很大困难,因此实现汉语被动句的自动识别对自然语言处理下游任务具有重要意义。本文构建了一个被动句语料库,提出了一个融合词性和动词论元框架信息的PC-BERT-CNN模型,对汉语被动句进行自动识别。实验结果表明,本文提出的模型能够准确地识别汉语被动句,其中有标记被动句识别F1值达到98.77%,无标记被动句识别F1值达到96.72%。”</abstract>
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%0 Conference Proceedings
%T 基于特征融合的汉语被动句自动识别研究(Automatic Recognition of Chinese Passive Sentences Based on Feature Fusion)
%A Hu, Kang
%A Qu, Weiguang
%A Wei, Tingxin
%A Zhou, Junsheng
%A Gu, Yanhui
%A Li, Bin
%S Proceedings of the 21st Chinese National Conference on Computational Linguistics
%D 2022
%8 October
%I Chinese Information Processing Society of China
%C Nanchang, China
%G Chinese
%F hu-etal-2022-ji
%X “汉语中的被动句根据有无被动标记词可分为有标记被动句和无标记被动句。由于其形态构成复杂多样,给自然语言理解带来很大困难,因此实现汉语被动句的自动识别对自然语言处理下游任务具有重要意义。本文构建了一个被动句语料库,提出了一个融合词性和动词论元框架信息的PC-BERT-CNN模型,对汉语被动句进行自动识别。实验结果表明,本文提出的模型能够准确地识别汉语被动句,其中有标记被动句识别F1值达到98.77%,无标记被动句识别F1值达到96.72%。”
%U https://aclanthology.org/2022.ccl-1.35
%P 384-394
Markdown (Informal)
[基于特征融合的汉语被动句自动识别研究(Automatic Recognition of Chinese Passive Sentences Based on Feature Fusion)](https://aclanthology.org/2022.ccl-1.35) (Hu et al., CCL 2022)
ACL