Finding factors
Stefanie Brüninghaus, Kevin D. Ashley · 1997
This paper presents preliminary work towards automatically assigning to full-text opinion tezts the applicable factors, that is, fact patterns influencing the outcome of a legal claim, to full-text opinion tezts, which are used in CATO's model of case-based legal argumentation.In spite of the fundamentally difierent representation and methods for comparing cases in a CATO-style case-based reasoning system versus text-retrieval systems, the paper provides evidence that there exists a connection between the notion of similarity under both paradigms.We consider the task as a classification problem, and apply machine learning methods, where CATO's Case Database of 147 tmde secret law cases are used as training instances.Since the generalization power of purely inductive algorithms, which rely on representation from conventional text-retrieval, does not measure up to the wmplexity of the concepts corresponding to the factors, the learning algorithms'performance is not satisfactory yet.%ing to address this problem, we discuss techniques that will allow integrating domain specific knowledge, about the use of cases in legal argumentation.and about the interrelations among the factors, which is expressed in CAT05 Factor Hiemrchy.Our hypothesis is that adding this knowledge un'll enhance performance, and that a successful system m-11 facilitate building legal case-based reasoning systems and legal research.