Identity Criteria of Common Nouns and dot-types for Copredication

S. Chatzikyriakidis, Zhaohui Luo
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引用次数: 6

Abstract

Copredication, especially when combined with quantification, provides interesting examples to support the idea that common nouns have their own identity criteria, as once argued for by Geach and subsequently studied by others. In this paper, revisiting the use of dot-types in modern type theories to model copredication, we show that, when both copredication and quantification are involved, CNs are not just types but should better be interpreted as types associated with their own identity criteria. In other words, formally, CNs are setoids – pairs whose first component is a type that interprets the domain of a CN and whose second component gives the identity criterion for that CN. For copredication with quantification, identity criteria play an essential role in giving a proper treatment of individuation and counting and hence constructing appropriate semantics to facilitate reasoning correctly. With CNs being setoids, the dot-type approach provides an adequate theory for copredication in general and for copredication with quantification in particular. It is further explained that the CNs-as-types approach is still the appropriate characterisation of our approach to interpreting CNs since, in phenomena that do not involve the interaction of copredication with quantification, the identity criteria of related CNs are essentially the same and can be safely ignored.
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常用名词与点型的同一性标准
共同解释,尤其是与量化结合使用时,提供了有趣的例子来支持普通名词有自己的身份标准的观点,正如Geach曾经提出的,随后被其他人研究的那样。在本文中,我们重新审视了现代类型理论中使用点类型来模拟协同作用,我们表明,当涉及到协同作用和量化时,神经网络不仅仅是类型,而是最好被解释为与自己的身份标准相关的类型。换句话说,形式上,CN是集合类-对,其第一个组件是解释CN域的类型,其第二个组件给出该CN的身份标准。在与量化的合作中,同一性标准在正确对待个性化和计数,从而构建适当的语义以促进正确推理方面起着至关重要的作用。由于神经网络是类集,点型方法为一般的协同作用,特别是与量化的协同作用提供了充分的理论。进一步解释说,cnns -as-types方法仍然是我们解释cnns方法的适当特征,因为在不涉及量化相互作用的现象中,相关cnns的识别标准本质上是相同的,可以安全地忽略。
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