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Evaluation of quantified propositions in generalized models of fuzzy quantification

机译:模糊量化广义模型中量化命题的评估

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摘要

The quantifiers found in natural language (NL) are not restricted to the absolute and proportional types usually considered in fuzzy set theory. In order to handle the wealth of NL quantifiers including quantifiers of exception ("all except about ten"), cardinal comparatives ("many more than") and others, it is necessary to consider generalized models of fuzzy quantification. Starting from an analysis in terms of semi-fuzzy quantifiers (specifications) and fuzzification mechanisms (prototypical models), the sequel develops a precise notion of generalized models which rests on a formalization of linguistic adequacy criteria. It also presents concrete examples of such models which generalize the FG-count and OWA approaches to fuzzy quantification. In order to let applications profit from the improved coverage and coherence of interpretations, the sequel is especially concerned with the issue of practical implementation. It presents efficient methods for implementing the main types of quantifying propositions which demonstrate the computational feasibility of the proposed models.
机译:自然语言(NL)中的量词不限于模糊集理论中通常考虑的绝对和比例类型。为了处理大量的NL量词,包括例外量词(“除约十”以外的所有量词),基数比较项(“多于”),以及其他的量词,有必要考虑模糊量化的广义模型。从对半模糊量词(规范)和模糊化机制(原型模型)的分析开始,续集发展出一种精确的泛化模型概念,该概念基于语言适当性标准的形式化。它还提供了此类模型的具体示例,这些模型将FG计数和OWA方法推广到模糊量化。为了让应用程序从解释的改进的覆盖范围和连贯性中受益,续篇特别关注实际实施的问题。它提供了用于实现量化命题主要类型的有效方法,这些方法证明了所提出模型的计算可行性。

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