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    <titleInfo>
        <title>基于阅读理解框架的中文事件论元抽取(Chinese Event Argument Extraction using Reading Comprehension Framework)</title>
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    <name type="personal">
        <namePart type="given">Min</namePart>
        <namePart type="family">Chen</namePart>
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    <name type="personal">
        <namePart type="given">Fan</namePart>
        <namePart type="family">Wu</namePart>
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    <name type="personal">
        <namePart type="given">Zhongqing</namePart>
        <namePart type="family">Wang</namePart>
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            <roleTerm authority="marcrelator" type="text">author</roleTerm>
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    <name type="personal">
        <namePart type="given">Peifeng</namePart>
        <namePart type="family">Li</namePart>
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    <name type="personal">
        <namePart type="given">Qiaoming</namePart>
        <namePart type="family">Zhu</namePart>
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        <dateIssued>2020-oct</dateIssued>
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            <title>Proceedings of the 19th Chinese National Conference on Computational Linguistics</title>
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            <publisher>Chinese Information Processing Society of China</publisher>
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                <placeTerm type="text">Haikou, China</placeTerm>
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    <abstract>传统的事件论元抽取方法把该任务当作句子中实体提及的多分类或序列标注任务,论元角色的类别在这些方法中只能作为向量表示,而忽略了论元角色的先验信息。实际上,论元角色的语义和论元本身有很大关系。对此,本文提议将其当作机器阅读理解任务,把论元角色表述为自然语言描述的问题,通过在上下文中回答这些问题来抽取论元。该方法更好地利用了论元角色类别的先验信息,在ACE2005中文语料上的实验证明了该方法的有效性。</abstract>
    <identifier type="citekey">chen-etal-2020-ji</identifier>
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        <url>https://aclanthology.org/2020.ccl-1.35</url>
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    <part>
        <date>2020-oct</date>
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            <start>376</start>
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%0 Conference Proceedings
%T 基于阅读理解框架的中文事件论元抽取(Chinese Event Argument Extraction using Reading Comprehension Framework)
%A Chen, Min
%A Wu, Fan
%A Wang, Zhongqing
%A Li, Peifeng
%A Zhu, Qiaoming
%S Proceedings of the 19th Chinese National Conference on Computational Linguistics
%D 2020
%8 oct
%I Chinese Information Processing Society of China
%C Haikou, China
%G Chinese
%F chen-etal-2020-ji
%X 传统的事件论元抽取方法把该任务当作句子中实体提及的多分类或序列标注任务,论元角色的类别在这些方法中只能作为向量表示,而忽略了论元角色的先验信息。实际上,论元角色的语义和论元本身有很大关系。对此,本文提议将其当作机器阅读理解任务,把论元角色表述为自然语言描述的问题,通过在上下文中回答这些问题来抽取论元。该方法更好地利用了论元角色类别的先验信息,在ACE2005中文语料上的实验证明了该方法的有效性。
%U https://aclanthology.org/2020.ccl-1.35
%P 376-389

Markdown (Informal)

[基于阅读理解框架的中文事件论元抽取(Chinese Event Argument Extraction using Reading Comprehension Framework)](https://aclanthology.org/2020.ccl-1.35) (Chen et al., CCL 2020)

• 基于阅读理解框架的中文事件论元抽取(Chinese Event Argument Extraction using Reading Comprehension Framework) (Chen et al., CCL 2020)

ACL

• Min Chen, Fan Wu, Zhongqing Wang, Peifeng Li, and Qiaoming Zhu. 2020. 基于阅读理解框架的中文事件论元抽取(Chinese Event Argument Extraction using Reading Comprehension Framework). In Proceedings of the 19th Chinese National Conference on Computational Linguistics, pages 376–389, Haikou, China. Chinese Information Processing Society of China.