如何在一个典型的感性语义学中建立法律话语的语言基础

L. McCarty
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引用次数: 3

摘要

在1995年和1997年的两篇论文中,我发展了一种法律论证的计算理论,但留下了一个关于“原型”这个关键概念的问题。机器学习的当代趋势现在为这个主题提供了新的视角。在本文中,我将描述我最近在“流形学习”方面的工作,以及在“深度学习”方面正在进行的一些工作。总的来说,这项工作导致了一种基于原型感知语义的逻辑语言,对法律理论有影响。本文的主要技术贡献是基于微分流形范畴(Man)的范畴逻辑,它比基于集合范畴(Set)或拓扑空间范畴(Top)的逻辑弱。本文还展示了如何将这种逻辑扩展到完整的法律话语语言(LLD),并提出了解决法律论证中难以捉摸的“连贯”问题的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
How to ground a language for legal discourse in a prototypical perceptual semantics
In a pair of papers from 1995 and 1997, I developed a computational theory of legal argument, but left open a question about the key concept of a "prototype." Contemporary trends in machine learning have now shed new light on the subject. In this paper, I will describe my recent work on "manifold learning," as well as some work in progress on "deep learning." Taken together, this work leads to a logical language grounded in a prototypical perceptual semantics, with implications for legal theory. The main technical contribution of the paper is a categorical logic based on the category of differential manifolds (Man), which is weaker than a logic based on the category of sets (Set) or the category of topological spaces (Top). The paper also shows how this logic can be extended to a full Language for Legal Discourse (LLD), and suggests a solution to the elusive problem of "coherence" in legal argument.
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