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        <title>基于句法特征的事件要素抽取方法(Syntax-aware Event Argument Extraction )</title>
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            <title>Proceedings of the 22nd Chinese National Conference on Computational Linguistics</title>
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    <abstract>“事件要素抽取(Event Argument Extraction, EAE)旨在从非结构化文本中提取事件参与要素。编码器—解码器(Encoder-Decoder)框架是处理该任务的一种常见策略,此前的研究大多只向编码器端输入文本的字词信息,导致模型泛化和远程依赖处理能力较弱。为此,本文提出一种融入句法信息的事件要素抽取模型。首先对文本分析得到成分句法解析树,将词性标签和各节点的句法成分标签编码,增强模型的文本表征能力。然后,本文提出了一种基于树结构的注意力机制(Tree-Attention)辅助模型更好地感知结构化语义信息,提高模型处理远距离依赖的能力。实验结果表明,本文所提方法相较于基线系统F1值提升2.02%,证明该方法的有效性。”</abstract>
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%0 Conference Proceedings
%T 基于句法特征的事件要素抽取方法(Syntax-aware Event Argument Extraction )
%A Yu, Zijian
%A Zhu, Tong
%A Chen, Wenliang
%Y Sun, Maosong
%Y Qin, Bing
%Y Qiu, Xipeng
%Y Jiang, Jing
%Y Han, Xianpei
%S Proceedings of the 22nd Chinese National Conference on Computational Linguistics
%D 2023
%8 August
%I Chinese Information Processing Society of China
%C Harbin, China
%G Chinese
%F yu-etal-2023-ji
%X “事件要素抽取(Event Argument Extraction, EAE)旨在从非结构化文本中提取事件参与要素。编码器—解码器(Encoder-Decoder)框架是处理该任务的一种常见策略,此前的研究大多只向编码器端输入文本的字词信息,导致模型泛化和远程依赖处理能力较弱。为此,本文提出一种融入句法信息的事件要素抽取模型。首先对文本分析得到成分句法解析树,将词性标签和各节点的句法成分标签编码,增强模型的文本表征能力。然后,本文提出了一种基于树结构的注意力机制(Tree-Attention)辅助模型更好地感知结构化语义信息,提高模型处理远距离依赖的能力。实验结果表明,本文所提方法相较于基线系统F1值提升2.02%,证明该方法的有效性。”
%U https://aclanthology.org/2023.ccl-1.18
%P 196-207

Markdown (Informal)

[基于句法特征的事件要素抽取方法(Syntax-aware Event Argument Extraction )](https://aclanthology.org/2023.ccl-1.18) (Yu et al., CCL 2023)

• 基于句法特征的事件要素抽取方法(Syntax-aware Event Argument Extraction ) (Yu et al., CCL 2023)

ACL

• Zijian Yu, Tong Zhu, and Wenliang Chen. 2023. 基于句法特征的事件要素抽取方法(Syntax-aware Event Argument Extraction ). In Proceedings of the 22nd Chinese National Conference on Computational Linguistics, pages 196–207, Harbin, China. Chinese Information Processing Society of China.