{"id":9471,"date":"2013-03-09T20:10:59","date_gmt":"2013-03-10T03:10:59","guid":{"rendered":"http:\/\/bucktownbell.com\/?p=9471"},"modified":"2013-03-09T20:10:59","modified_gmt":"2013-03-10T03:10:59","slug":"information-extraction-and-synthesis-laboratory","status":"publish","type":"post","link":"http:\/\/bucktownbell.com\/?p=9471","title":{"rendered":"Information Extraction and Synthesis Laboratory"},"content":{"rendered":"<blockquote><p>Cross-document coreference resolution is the task of grouping the entity mentions in a collection of documents into sets that each represent a distinct entity. It is central to knowledge base construction and also useful for joint inference with other NLP components. Obtaining large, organic labeled datasets for training and testing cross-document coreference has previously been difficult. We use a method for automatically gathering massive amounts of naturally-occurring cross-document reference data to create the Wikilinks dataset comprising of 40 million mentions over 3 million entities. Our method is based on finding hyperlinks to Wikipedia from a web crawl and using anchor text as mentions. In addition to providing large-scale labeled data without human effort, we are able to include many styles of text beyond newswire and many entity types beyond people.<\/p><\/blockquote>\n<p>via <a href=\"http:\/\/www.iesl.cs.umass.edu\/data\/wiki-links\">Wikilinks &#8211; Information Extraction and Synthesis Laboratory<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Cross-document coreference resolution is the task of grouping the entity mentions in a collection of documents into sets that each represent a distinct entity. It is central to knowledge base construction and also useful for joint inference with other NLP &hellip; <a href=\"http:\/\/bucktownbell.com\/?p=9471\">Continue reading <span class=\"meta-nav\">&rarr;<\/span><\/a><\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[54],"tags":[170,1058,629,986],"class_list":["post-9471","post","type-post","status-publish","format-standard","hentry","category-programming","tag-data-modeling","tag-datasets","tag-information-theory","tag-search-engines"],"_links":{"self":[{"href":"http:\/\/bucktownbell.com\/index.php?rest_route=\/wp\/v2\/posts\/9471","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/bucktownbell.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/bucktownbell.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/bucktownbell.com\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"http:\/\/bucktownbell.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=9471"}],"version-history":[{"count":0,"href":"http:\/\/bucktownbell.com\/index.php?rest_route=\/wp\/v2\/posts\/9471\/revisions"}],"wp:attachment":[{"href":"http:\/\/bucktownbell.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=9471"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/bucktownbell.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=9471"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/bucktownbell.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=9471"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}