Stefanie Brüninghaus

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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(More)
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(More)
Since assigning indicies to textual cases is very time-consuming and can impede the development of CBR systems, methods to automate the task are desirable. In this paper, we present a machine learning approach that helps to bootstrap the development of a larger case base from a small collection of marked-up case summaries. It uses the marked-up sentences as(More)
This paper reports preliminary work on developing methods automatically to index cases described in text so that a case-based reasoning system can reason with them. We are employing machine learning algorithms to classify full-text legal opinions in terms of a set of predefined concepts. These factors, representing factual strengths and weaknesses in the(More)
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(More)