关于术语集在模糊语言偏好模型中的对对比的语义.
Ana Nieto-Morote1, Francisco Ruz-Vila2
1Project Engineering Department, Polytechnic University of Cartagena, c/Dr. Fleming, s/n, 30202 Cartagena, Spain.
Entropy (Basel, Switzerland)
|May 27, 2023
概括
本研究定义了在偏好建模中将会员功能分配给语言术语的程序. 它区分了削弱和强化对冲,使用不同的数学模型来定义每个术语的语义.
科学领域:
- 信息理论,概率和统计学
- 语言学的语言学.
- 模糊的集合理论 模糊的集合理论
背景情况:
- 语言术语和对冲显著影响偏好建模.
- 了解语言术语的语义需要分析固有的特征和上下文因素.
- 现有的方法可能无法充分捕捉对冲引入的细微含义.
研究的目的:
- 根据语言术语内在的语义特征,定义一种将会员功能赋予语言术语的程序.
- 在偏好建模上下文中确定语言术语的语义.
- 区分和建模弱化和强化对冲的独特语义影响.
主要方法:
- 语言概念的分析:语言的互补性,上下文的影响,对副词意义的对冲效应.
- 模糊关系微积分用于削弱对冲的应用.
- 利用来自替代集合理论的地平线转移模型用于增强对冲.
主要成果:
- 成员函数的特异性,和位置是由对冲语义学决定的.
- 削弱对冲在语言上是不包容的;加强对冲在语言上是包容的.
- 诱导方法产生了非均的,非对称的三角形模糊数分布.
结论:
- 建立了基于语言术语特征的会员职能分配的新程序.
- 需要不同的数学方法来建模弱化和强化对冲的不同语义.
- 拟议的方法在偏好建模中更准确地表示术语集语义.
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