English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Open

Presents the pdf analysis as premium feature

PDF Analysis

Insights

Presents the summarisation as premium feature

Summarisation

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

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.

How to ground a language for legal discourse in a prototypical perceptual semantics