模糊系统中模糊概念的模糊语义模型

Yingxu Wang
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引用次数: 2

摘要

语言语义的模糊特性是认知语言学、模糊系统和计算语言学中机器支持的自然语言处理的核心问题。为了缩小人类与认知模糊系统之间的差距,需要寻求一种严格描述和操纵模糊语义的形式化方法。模糊概念的数学模型被严格地描述为属性、对象、关系和限定的模糊集合的上层结构,它是语义分析中表示语言实体的模糊语义的基本单位。将形式模糊概念推广到考虑模糊修饰语和限定词的复杂结构中。提出了一种处理复合模糊语义的代数方法,将其作为一个从模糊概念到确定语义的演绎过程。模糊语义推理的指称数学结构不仅解释了人类语义及其理解的模糊性,而且使认知机器和模糊系统能够在认知语言学、认知计算和计算智能中模仿人类的模糊推理机制。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fuzzy Semantic Models of Fuzzy Concepts in Fuzzy Systems
The fuzzy properties of language semantics are a central problem towards machine-enabled natural language processing in cognitive linguistics, fuzzy systems, and computational linguistics. A formal method for rigorously describing and manipulating fuzzy semantics is sought for bridging the gap between humans and cognitive fuzzy systems. The mathematical model of fuzzy concepts is rigorously described as a hyperstructure of fuzzy sets of attributes, objects, relations, and qualifications, which serves as the basic unit of fuzzy semantics for denoting languages entities in semantic analyses. The formal fuzzy concept is extended to complex structures where fuzzy modifiers and qualifiers are considered. An algebraic approach is developed to manipulate composite fuzzy semantic as a deductive process from a fuzzy concept to the determined semantics. The denotational mathematical structure of fuzzy semantic inference not only explains the fuzzy nature of human semantics and its comprehension, but also enables cognitive machines and fuzzy systems to mimic the human fuzzy inference mechanisms in cognitive linguistics, cognitive computing, and computational intelligence.
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