  In this document I present an approach to answer validation and reranking for
question answering (QA) systems. A cased-based reasoning (CBR) system judges
answer candidates for questions from annotated answer candidates for earlier
questions. The promise of this approach is that user feedback will result in
improved answers of the QA system, due to the growing case base. In the paper,
I present the adequate structuring of the case base and the appropriate
selection of relevant similarity measures, in order to solve the answer
validation problem. The structural case base is built from annotated MultiNet
graphs, which provide representations for natural language expressions, and
corresponding graph similarity measures. I cover a priori relations to
experienced answer candidates for former questions. I compare the CBR System
results to current approaches in an experiment integrating CBR into an existing
framework for answer validation and reranking. This integration is achieved by
adding CBR-related features to the input of a learned ranking model that
determines the final answer ranking. In the experiments based on QA@CLEF
questions, the best learned models make heavy use of CBR features. Observing
the results with a continually growing case base, I present a positive effect
of the size of the case base on the accuracy of the CBR subsystem.
