@article{Burke_Hammond_Kulyukin_Lytinen_Tomuro_Schoenberg_1997, title={Question Answering from Frequently Asked Question Files: Experiences with the FAQ FINDER System}, volume={18}, url={https://ojs.aaai.org/aimagazine/index.php/aimagazine/article/view/1294}, DOI={10.1609/aimag.v18i2.1294}, abstractNote={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.}, number={2}, journal={AI Magazine}, author={Burke, Robin D. and Hammond, Kristian J. and Kulyukin, Vladimir and Lytinen, Steven L. and Tomuro, Noriko and Schoenberg, Scott}, year={1997}, month={Jun.}, pages={57} }