Automatically understanding the rhetorical roles of sentences in a legal case judgement is an important problem to solve, since it can help in several downstream tasks like summarization of legal judgments, legal search, and so on.
We present the design of a system for making sense of conflicting rules expressed in a fragment of the prominent controlled natural language ACE, yet extended with means of expressing defeasible rules in the form of normality assumptions.
Contrastive opinion mining is essential in identifying, extracting and organising opinions from user generated texts.
The paper investigates the extent of the support semi-automatic analysis can provide for the specific task of assigning Hohfeldian relations of Duty, using the General Architecture for Text Engineering tool for the automated extraction of Duty instances and the bearers of associated roles.
The paper provides a corpus and experimental results with material derived from a real bar exam, treating the problem as a form of textual entailment from the question to an answer.