A Case Based Reasoning Approach for Answer Reranking in Question Answering
Karl-Heinz Weis

TL;DR
This paper introduces a case-based reasoning approach for answer reranking in question answering systems, leveraging annotated answer cases and similarity measures to improve answer validation and ranking accuracy.
Contribution
It presents a novel structuring of the case base using MultiNet graphs and demonstrates how CBR features enhance answer reranking in QA systems.
Findings
Growing case base improves CBR accuracy
CBR features significantly boost ranking performance
Structural case base effectively supports answer validation
Abstract
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…
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