In this paper, we present methods for automatically finding abstract, legally relevant concepts in case texts and demonstrate how they can be used to make predictions of case outcomes, given the texts as inputs.In a set of experiments to test these methods, we focus on the open question of how best to represent legal text for finding abstract concepts. We compare different ways of representing legal case texts in order to test whether adding domain knowledge and some linguistic information can improve performance.We found that replacing individual names by roles in the case texts led to better indexing, and that adding certain syntactic and semantic information, in the form of Propositional Patterns that capture a sense of "who did what", led to better prediction. Our experiments also showed that of three learning algorithms, Nearest Neighbor worked best in learning how to identify indexing concepts in texts.In these experiments, we introduced a prototype system that can reason with text cases; it analyzes a case, predicts its outcome considering other cases in the database, and explains the prediction, all starting with a textual description of the case's facts as input.

In this paper, we introduce IBP, an algorithm that combines reasoning with an abstract domain model and case-based reasoning techniques to predict the outcome of case-based legal arguments. Unlike the predictions generated by statistical or machine-learning techniques, IBP's predictions are accompanied by explanations.We describe an empirical evaluation of IBP, in which we compare our algorithm to prediction based on Hypo's and CATO's relevance criteria, and to a number of widely used machine learning algorithms. IBP reaches higher accuracy than all competitors, and hypothesis testing shows that the observed differences are statistically significant. An ablation study indicates that both sources of knowledge in IBP contribute to the accuracy of its predictions.

The prohibitive cost of assigning indices to textual cases is a major obstacle for the practical use of AI and Law systems supporting reasoning and arguing with cases. While progress has been made toward extracting certain facts from well-structured case texts or classifying case abstracts under Key Number concepts, these methods still do not suffice for the complexity of indexing concepts in CBR systems.

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.