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Noun compound semantics: Linguistic and general-purpose reasoning.

机译:名词复合语义:语言和通用推理。

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摘要

Noun compounds resist standard compositional semantic analysis because the modification relation is implicit and because nouns have been thought to lack argument structure. Early analyses provided argument positions for nouns using phrase structure rules that licensed phonologically null predicates. Recent work proposes detailed nominal argument structure and enriched semantic composition. Despite the shift in emphasis from syntax to the lexicon, the prevailing approaches still encode meanings grammatically.; This dissertation offers three main contributions to the semantic analysis and processing of compounds. First, I problematize the traditional distinctions made in analyses of compounds, beginning with the strict separation of compounds that have deverbal heads from those with non-deverbal heads. I argue that these classes participate in the same sorts of modification relations. I also present a novel classification based on the logical structure of denotations, and I offer independent motivation for the classification through an analysis of word-order constraints. The second main contribution is to recast interpretation as a process of general-purpose reasoning rather than linguistic rule application. The third contribution is a model that uses measures of concept and word probabilities and inferencing procedures utilizing a general ontology.; The denotations generated by the system are used in several tasks: disambiguation among semantic classes, as input for generating informative paraphrases, and as queries to select potential referents in a 3D graphical environment. The disambiguation task tests the system's ability to make certain semantic distinctions regarding the type of modification and the logical form of the denotation. The paraphrasing task allows naive judges to rate paraphrases based on denotations that are generated using different degrees of intelligence available in the system. The results generally demonstrate above-baseline performance, suggesting that the approach represents a fruitful combination of symbolic and statistical methods for generating and ranking denotations.
机译:名词复合词可以抵抗标准的成分语义分析,因为修饰关系是隐式的,并且因为名词被认为缺乏论点结构。早期分析使用短语结构规则为名词提供了论据位置,该短语结构规则在语音上为空谓词。最近的工作提出了详细的名义论证结构和丰富的语义组成。尽管重点从语法转移到了词典,但主流方法仍在语法上编码含义。本文为化合物的语义分析和处理提供了三个主要的贡献。首先,我对化合物分析中的传统区别提出了质疑,首先要严格区分具有副标题和非Deverbal标题的化合物。我认为这些类都参与相同类型的修改关系。我还提出了一种基于指称逻辑结构的新颖分类,并且我通过分析词序约束为分类提供了独立的动机。第二个主要贡献是将解释重铸为通用推理而不是语言规则应用的过程。第三贡献是使用概念和单词概率的度量以及利用一般本体的推理过程的模型。系统生成的符号用于以下任务:语义类之间的歧义消除,生成信息性复述的输入以及在3D图形环境中选择潜在参考对象的查询。消歧任务测试系统在修饰类型和逻辑形式上做出某些语义区分的能力。释义任务允许幼稚的法官根据使用系统中可用的不同智能程度生成的表示法对释义进行评分。结果总体上证明高于基线,表明该方法代表了用于生成和排序符号的符号方法和统计方法的有效结合。

著录项

  • 作者

    Arehart, Mark David.;

  • 作者单位

    University of Michigan.;

  • 授予单位 University of Michigan.;
  • 学科 Language Linguistics.
  • 学位 Ph.D.
  • 年度 2003
  • 页码 182 p.
  • 总页数 182
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 语言学;
  • 关键词

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