Developing Mapping and Evaluation Techniques for Textual Case-Based Reasoning

Kevin D. Ashley and Stefanie Brüninghaus

When Case-Based Reasoning is employed in a domain where the cases are texts, case representations have to be powerful enough for reasoning about problem situations by comparing them symbolically to cases in the case base. Information Retrieval Techniques, however, permit only shallow statistical inferences based on easily countable word occurence, and are thus inappropriate for Textual CBR. Instead, methods for mapping texts to a structured representation are needed. In the paper, we present our approach to indexing legal cases for the use in a case-based, legal argumentation system. We are working with machine learning methods to automatically classify texts under the concepts corresponding the CBR case representation. Furthermore, we discuss how knowledge about the domain and the CBR application can be integrated.

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