{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/105973"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/105973","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Learning to answer questions from semi-structured knowledge sources","abstract":"Question answering is an efficient and convenient way for humans to make use of the massive amount of information on the Web. I start with an interesting source of information -- infoboxes in Wikipedia that summarize factoid knowledge -- and develop a comprehensive approach to answering questions with high precision. I first build a system to access data in infoboxes in a structured manner. I use the system to construct a crowdsourced dataset of over 15,000 high-quality, diverse questions. 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