{"id":453870,"date":"2018-01-11T18:48:56","date_gmt":"2018-01-12T02:48:56","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=453870"},"modified":"2018-10-16T20:10:17","modified_gmt":"2018-10-17T03:10:17","slug":"laseweb-automating-search-strategies-semi-structured-web-data-2","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/laseweb-automating-search-strategies-semi-structured-web-data-2\/","title":{"rendered":"LaSEWeb: Automating Search Strategies over Semi-structured Web Data"},"content":{"rendered":"
\n

We show how to programmatically model processes that humans use when extracting answers to queries (e.g., “Who invented typewriter?”, “List of Washington national parks”) 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<\/div>\n","protected":false},"excerpt":{"rendered":"

We show how to programmatically model processes that humans use when extracting answers to queries (e.g., “Who invented typewriter?”, “List of Washington national parks”) 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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