Strategies for Improving the Efficacy of Fusion Question Answering Systems

José Antonio Robles-Flores, Gregory Schymik, Julie Smith-David and Robert St. Louis
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José Antonio Robles-Flores: ESAN University, Peru
Gregory Schymik: Arizona State University, USA
Julie Smith-David: Arizona State University, USA
Robert St. Louis: Arizona State University, USA

International Journal of Business Intelligence Research (IJBIR), 2011, vol. 2, issue 1, 46-63

Abstract: Web search engines typically retrieve a large number of web pages and overload business analysts with irrelevant information. One approach that has been proposed for overcoming some of these problems is automated Question Answering (QA). This paper describes a case study that was designed to determine the efficacy of QA systems for generating answers to original, fusion, list questions (questions that have not previously been asked and answered, questions for which the answer cannot be found on a single web site, and questions for which the answer is a list of items). Results indicate that QA algorithms are not very good at producing complete answer lists and that searchers are not very good at constructing answer lists from snippets. These findings indicate a need for QA research to focus on crowd sourcing answer lists and improving output format.

Date: 2011
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