Question answering from the web using knowledge annotation and knowledge mining techniques

We present a strategy for answering fact-based natural language questions that is guided by a characterization of realworld 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 semistructured 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. Categories and Subject Descriptors H.3.4 [Information Systems]: Information Storage and Retrieval, Systems and Software General Terms Design, Performance Keywords semistructured data, data-redundancy

• Type: Conference
• Year: 2003
• Where: CIKM
• Authors: Jimmy J. Lin, Boris Katz
• Keywords: CIKM 2003, Fact-based Natural Language, Knowledge Mining, Natural Language