Question Answering From The Web Using Knowledge Annotation And Knowledge Mining Techniques

Abstract

We present a strategy for answering fact-based natural language questions that is guided by a characterization of real-world user queries. Our approach, implemented in a system called Aranea, extracts answers from the Web using two different techniques: <i>knowledge annotation</i> and <i>knowledge mining</i>. Knowledge annotation is an approach to answering large classes of frequently occurring questions by utilizing semi\-structured and structured Web sources. Knowledge mining is a statistical approach that leverages massive amounts of Web data to overcome many natural language processing challenges. We have integrated these two different paradigms into a question answering system capable of providing users with concise answers that directly address their information needs.