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Data-driven deep-syntactic dependency parsing

机译:数据驱动的深语法依赖解析

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摘要

'Deep-syntactic' dependency structures that capture the argumentative, attributive and co-ordinative relations between full words of a sentence have a great potential for a number of NLP-applications. The abstraction degree of these structures is in between the output of a syntactic dependency parser (connected trees defined over all words of a sentence and language-specific grammatical functions) and the output of a semantic parser (forests of trees defined over individual lexemes or phrasal chunks and abstract semantic role labels which capture the frame structures of predicative elements and drop all attributive and coordinative dependencies). We propose a parser that provides deep-syntactic structures. The parser has been tested on Spanish, English and Chinese.
机译:捕获句子完整单词之间的辩论性,定语性和协调关系的“深度句法”依赖结构在许多NLP应用中具有巨大潜力。这些结构的抽象度介于语法相关性解析器的输出(在句子的所有单词上定义的连接树和特定于语言的语法功能)和语义解析器的输出(在单个词素或短语上定义的树的森林)之间块和抽象语义角色标签,它们捕获谓词元素的框架结构,并删除所有定语和协调依赖性。我们提出了一种提供深语法结构的解析器。该解析器已经过西班牙语,英语和中文的测试。

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  • 来源
    《Natural language engineering》 |2016年第6期|939-974|共36页
  • 作者单位

    Pompeu Fahra University, Natural Language Processing Group, Roc Boronat 158, 08018 Barcelona, Spain;

    Google Inc. London, 76 Buckingham Palace Road, London SW1W 9TQ, UK;

    Pompeu Fahra University, Natural Language Processing Group, Roc Boronat 158, 08018 Barcelona, Spain;

    Pompeu Fahra University, Natural Language Processing Group, Roc Boronat 158, 08018 Barcelona, Spain,Catalan Institute for Research and Advanced Studies (ICREA), Lluis Companys, 23, 08010 Barcelona, Spain;

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