Improving FAQfinder’s Performance: Setting Parameters by Genetic Programming

Machine Learning in Information Access

Volume

Papers from the 1996 AAAI Spring Symposium

Contents

As the quantity of information available on the Internet continues to increase, so do the attempts to harness that information. One such attempt is the University of Chicago’s FAQfiuder project; given a natural language English question, the FAQfmder system attempts first to locate the most relevant list of Frequently Asked Questions (FAQs), and then to find the question on that fist most similar to the one asked by the user. Since FAQs nnmher in the thousands sad each one represents a concentration of information in a specific subject area, the FAQfinder system offers a means of accessing useful infonnetion in a straightforward manner.