We show how to programmatically model processes that humans use when extracting answers to queries (e.g., \u201cWho invented typewriter?\u201d, \u201cList of Washington national parks\u201d) from semi-structured Web pages returned by a search engine. This modeling enables various applications including automating repetitive search tasks, and helping search engine developers design micro-segments of factoid questions.<\/p>\n
We describe the design and implementation of a domain-specific language that enables extracting data from a webpage based on its structure, visual layout, and linguistic patterns. We also describe an algorithm to rank multiple answers extracted from multiple webpages.<\/p>\n
On 100,000+ queries (across 7 micro-segments) obtained from Bing logs, our system LaSEWeb answered queries with an average recall of 71%. Also, the desired answer(s) were present in top-3 suggestions for 95%+ cases.<\/p>\n","protected":false},"excerpt":{"rendered":"
We show how to programmatically model processes that humans use when extracting answers to queries (e.g., \u201cWho invented typewriter?\u201d, \u201cList of Washington national parks\u201d) from semi-structured Web pages returned by a search engine. This modeling enables various applications including automating repetitive search tasks, and helping search engine developers design micro-segments of factoid questions. 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