An exploration of the principles underlying redundancy-based factoid question answering

Abstract

The so-called “redundancy-based” approach to question answering represents a successful strategy for mining answers to factoid questions such as “Who shot Abraham Lincoln?” from the World Wide Web. Through contrastive and ablation experiments with Aranea, a system that has performed well in several TREC QA evaluations, this work examines the underlying assumptions and principles behind redundancy-based techniques. Specifically, we develop two theses: that stable characteristics of data redundancy allow factoid systems to rely on external “black box” components, and that despite embodying a data-driven approach, redundancy-based methods encode a substantial amount of knowledge in the form of heuristics. Overall, this work attempts to address the broader question of “what really matters” and to provide guidance for future researchers.