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Abstraction and Generalisation in Semantic Role Labels: PropBank, VerbNet or both?

机译:语义角色标签中的抽象和泛化:PropBank,VerbNet还是两者?

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Semantic role labels are the representation of the grammatically relevant aspects of a sentence meaning. Capturing the nature and the number of semantic roles in a sentence is therefore fundamental to correctly describing the interface between grammar and meaning. In this paper, we compare two annotation schemes, Prop-Bank and VerbNet, in a task-independent, general way, analysing how well they fare in capturing the linguistic generalisations that are known to hold for semantic role labels, and consequently how well they grammaticalise aspects of meaning. We show that VerbNet is more verb-specific and better able to generalise to new semantic role instances, while PropBank better captures some of the structural constraints among roles. We conclude that these two resources should be used together, as they are complementary.
机译:语义角色标签是句子含义的语法相关方面的表示。因此,捕捉句子中的性质和语义角色的数量是正确描述语法和含义之间的界面的基础。在本文中,我们比较了两种注释方案,PROP-BANK和Verbnet,以任务独立的一般方式进行分析,分析它们在捕获所知的语言概括时的票价如何,并且因此它们的程度如何意义的语法方面。我们表明,VerbNet更具动词特定,更能够概括为新的语义角色实例,而Propbank则更好地捕获角色之间的一些结构约束。我们得出结论,应该一起使用这两个资源,因为它们是互补的。

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