{"id":1555,"date":"2020-11-27T10:50:44","date_gmt":"2020-11-27T09:50:44","guid":{"rendered":"https:\/\/saarland-informatics-campus.de\/project\/big-data-for-text-next-generation-text-understanding-and-analysis\/"},"modified":"2020-11-27T10:50:44","modified_gmt":"2020-11-27T09:50:44","slug":"big-data-for-text-next-generation-text-understanding-and-analysis","status":"publish","type":"project","link":"https:\/\/saarland-informatics-campus.de\/en\/project\/big-data-for-text-next-generation-text-understanding-and-analysis\/","title":{"rendered":"Big Data for Text: Next-Generation Text Understanding and Analysis"},"content":{"rendered":"

News portals and social media are rich information sources, for example for predicting stock market trends. Today, numerous service providers allow for searching large text collections by feeding their search engines with descriptive keywords. Keywords tend to be highly ambiguous, though, and quickly show the limits of current search technologies. Computer scientists from Saarbr\u00fccken developed a novel text analysis technology that considerably improves searching very large text collections by means of artificial intelligence. Beyond search, this technology also assists authors in researching and even in writing texts by automatically providing background information and suggesting links to relevant web sites.<\/p>\n

Ambiverse, a spin-off company from the Max Planck Institute for Informatics in Saarbr\u00fccken, will be presenting this novel technology during CeBIT 2016 in Hannover from 14 to 18 March at Saarland\u2019s research booth.<\/p>\n

more information<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

News portals and social media are rich information sources, for example for predicting stock market trends. Today, numerous service providers allow for searching large text collections by feeding their search engines with descriptive keywords. Keywords tend to be highly ambiguous, though, and quickly show the limits of current search technologies. Computer scientists from Saarbrücken developed […]<\/p>\n","protected":false},"author":41,"featured_media":1553,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_et_pb_use_builder":"","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"project_category":[45,36],"project_tag":[],"class_list":["post-1555","project","type-project","status-publish","has-post-thumbnail","hentry","project_category-2016-en","project_category-cebit-en"],"yoast_head":"\nBig Data for Text: Next-Generation Text Understanding and Analysis - Saarland Informatics Campus<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/saarland-informatics-campus.de\/en\/project\/big-data-for-text-next-generation-text-understanding-and-analysis\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Big Data for Text: Next-Generation Text Understanding and Analysis - Saarland Informatics Campus\" \/>\n<meta property=\"og:description\" content=\"News portals and social media are rich information sources, for example for predicting stock market trends. 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