  Rules are pervasive in the law. In the context of computer engineering, the
translation of legal text to algorithmic form is seemingly direct. In large
part, law may be a ripe field for expert systems and machine learning. For
engineers, existing law appears formulaic and logically reducible to "if, then"
statements. The underlying assumption is that the legal language is both
self-referential and universal. Moreover, description is considered distinct
from interpretation; that in describing the law, the language is seen as
quantitative and objectifiable. Nevertheless, is descriptive formal language
purely dissociative? From the logic machine of the 1970s to the modern fervor
for artificial intelligence (AI), governance by numbers is making a persuasive
return. Could translation be possible? The project follows a fundamentally
semantic conundrum: what is the significance of "meaning" in legal language?
The project, therefore, tests translation by deconstructing sentences from
existing legal judgments to their constituent factors. Definitions are then
extracted in accordance with the interpretations of the judges. The intent is
to build an expert system predicated on alleged rules of legal reasoning. The
authors apply both linguistic modelling and natural language processing
technology to parse the legal judgments. The project extends beyond prior
research in the area, combining a broadly statistical model of context with the
relative precision of syntactic structure. The preliminary hypothesis is that,
by analyzing the components of legal language with a variety of techniques, we
can begin to translate law to numerical form.
