Stefanie Brüninghaus

Learn More
This commentary provides a definition of textual case-based reasoning (TCBR) and surveys research contributions according to four research questions. We also describe how TCBR can be distinguished from text mining and information retrieval. We conclude with potential directions for TCBR research. 1 What is textual case-based reasoning? Case-based reasoning(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)
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)
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)
Abstract. This paper presents methods that support automatically finding abstract indexing concepts in textual cases and demonstrates how these cases can be used in an interpretive CBR system to carry out case-based argumentation and prediction from text cases. We implemented and evaluated these methods in SMILE+IBP, which predicts the outcome of legal(More)
Work on a computer program called SMILE + IBP (SMart Index Learner Plus Issue-Based Prediction) bridges case-based reasoning and extracting information from texts. The program addresses a technologically challenging task that is also very relevant from a legal viewpoint: to extract information from textual descriptions of the facts of decided cases and(More)
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(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)