Question Answering from Frequently Asked Question Files: Experiences with the FAQ FINDER System

Robin D. Burke, Kristian J. Hammond, Vladimir Kulyukin, Steven L. Lytinen, Noriko Tomuro, Scott Schoenberg

Abstract


This article describes FAQ FINDER, a natural language question-answering system that uses files of frequently asked questions as its knowledge base. Unlike AI question-answering systems that focus on the generation of new answers, FAQ FINDER retrieves existing ones found in frequently asked question files. Unlike information-retrieval approaches that rely on a purely lexical metric of similarity between query and document, FAQ FINDER uses a semantic knowledge base (WORDNET) to improve its ability to match question and answer. We include results from an evaluation of the system's performance and show that a combination of semantic and statistical techniques works better than any single approach.

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DOI: http://dx.doi.org/10.1609/aimag.v18i2.1294

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