%0 Conference Paper %B Proceedings of the twelfth international conference on Information and knowledge management %D 2003 %T Question answering from the web using knowledge annotation and knowledge mining techniques %A Jimmy Lin %A Katz,Boris %K data-redundancy %K semistructured data %X 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: knowledge annotation and knowledge mining. 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. %B Proceedings of the twelfth international conference on Information and knowledge management %S CIKM '03 %I ACM %C New York, NY, USA %P 116 - 123 %8 2003/// %@ 1-58113-723-0 %G eng %U http://doi.acm.org/10.1145/956863.956886 %R 10.1145/956863.956886