Author: Steven L. Lytinen
and Noriko
Tomuro
Journal-ref: In Papers from the 2002 AAAI Spring Symposium on Mining
Answers from Texts and Knowledge Bases, Stanford, CA.
One useful way to find the answer to a question is to search a library of previously-answered questions. This is the idea behind FAQFinder, a Web-based natural language question-answering system which uses Frequently Asked Questions (FAQ) files to answer users' questions. FAQFinder tries to answer a user's question by retrieving a similar FAQ question, if one exists, and its answer. FAQFinder uses several metrics to judge the similarity of user and FAQ questions. In this paper, we discuss a metric based on question type, which we recently added to the system. We discuss the taxonomy of question types used, and present experimental results which indicate that the incorporation of question type information has substantially improved FAQFinder's performance.
Paper: Full paper (pdf 203k)