细化UCCA的隐式参数注释
Refining Implicit Argument Annotation for UCCA
摘要：
谓词论元结构分析是语篇意义表征的核心内容。事实上，一些参数在句子中没有明确提到，这会导致语言理解的歧义，并使机器难以正确地解释文本。然而，只有很少的资源代表隐含的作用，自然语言理解，现有的研究在自然语言处理只是粗略的区别类别的参数省略从语言形式。本文在泛概念认知标注的基础层之上，提出了一种细粒度隐式论元标注的类型学。隐式论元分类是由隐式角色解释理论驱动的，包括六种类型：指示语、类属语、体裁型、可识别类型、非特定类型和迭代集。我们重新访问了UCCA EWT语料库的一部分，提供了一个新的数据集与细化层的注释，并与其他方案进行了比较分析，以验证我们的设计。
英文摘要：
Predicate-argument structure analysis is a central component in meaning representations of text. The fact that some arguments are not explicitly mentioned in a sentence gives rise to ambiguity in language understanding, and renders it difficult for machines to interpret text correctly. However, only few resources represent implicit roles for NLU, and existing studies in NLP only make coarse distinctions between categories of arguments omitted from linguistic form. This paper proposes a typology for fine-grained implicit argument annotation on top of Universal Conceptual Cognitive Annotation's foundational layer. The proposed implicit argument categorisation is driven by theories of implicit role interpretation and consists of six types: Deictic, Generic, Genre-based, Type-identifiable, Non-specific, and Iterated-set. We exemplify our design by revisiting part of the UCCA EWT corpus, providing a new dataset annotated with the refinement layer, and making a comparative analysis with other schemes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
