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Stefanie Brüninghaus

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AILAW Journal 2009 Journal Article

Automatically classifying case texts and predicting outcomes

Kevin D. Ashley
Stefanie Brüninghaus

Abstract 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 apply that information to predict the outcomes of new cases. The program attempts to automatically classify textual descriptions of the facts of legal problems in terms of Factors, a set of classification concepts that capture stereotypical fact patterns that effect the strength of a legal claim, here trade secret misappropriation. Using these classifications, the program can evaluate and explain predictions about a problem’s outcome given a database of previously classified cases. This paper provides an extended example illustrating both functions, prediction by IBP and text classification by SMILE, and reports empirical evaluations of each. While IBP’s results are quite strong, and SMILE’s much weaker, SMILE + IBP still has some success predicting and explaining the outcomes of case scenarios input as texts. It marks the first time to our knowledge that a program can reason automatically about legal case texts.

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KER Journal 2005 Journal Article

Textual case-based reasoning

Rosina O. Weber
Kevin D. Ashley
Stefanie Brüninghaus

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.

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AAAI Conference 1994 Short Paper

DANIEL: Integrating Case-Based and Rule-Based Reasoning in Law

Stefanie Brüninghaus

This paper introduces DANlEL, an architecture for the integration of case-based reasoning and rule-based reasoning for legal interpretation. Rather than interleaving the reasoners and assuming their complementarity, like in previous approaches, they are applied concurrently. Conflicting interpretations are handled explicitly, based on domain knowledge and on the notion of redundancy. The principal problems of legal interpretation are the lack of deep models for legal reasoning, the existence of inherently ill-defined predicates and the frequent use of open-textured concepts, as pointed out in (Rissland and Skalak 1991). A hybrid approach to representing the legal sources and the use of meta-knowledge seems to be appropriate to solve these problems. The scope of DANIEL is not limited to this particular domain, since the noted difficulties do not occur exclusively, but prototypically in the law.

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