{"id":184570,"date":"2009-12-03T00:00:00","date_gmt":"2009-12-07T17:18:54","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/natural-language-processing-from-scratch\/"},"modified":"2016-09-09T09:49:19","modified_gmt":"2016-09-09T16:49:19","slug":"natural-language-processing-from-scratch","status":"publish","type":"msr-video","link":"https:\/\/www.microsoft.com\/en-us\/research\/video\/natural-language-processing-from-scratch\/","title":{"rendered":"Natural Language Processing From Scratch"},"content":{"rendered":"<div class=\"asset-content\">\n<p>We will describe recent advances in deep learning techniques for<br \/>\nNatural Language Processing (NLP). Traditional NLP approaches favour<br \/>\nshallow systems, possibly cascaded, with adequate hand-crafted<br \/>\nfeatures. In this work we purposefully try to disregard domain-<br \/>\nspecific knowledge in favor of large-scale semi-supervised end-to-end<br \/>\nlearning. Our systems include several feature layers, with increasing<br \/>\nabstraction level at each layer, that is, a multi-layer neural<br \/>\nnetwork. We will describe training techniques that easily scale to a<br \/>\nbillion of unlabeled words. We will discuss multi-tasking different<br \/>\ntasks and end-to-end structured output learning. We will demonstrate<br \/>\nstate-of-the-art accuracies with considerable speedups.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>We will describe recent advances in deep learning techniques for Natural Language Processing (NLP). Traditional NLP approaches favour shallow systems, possibly cascaded, with adequate hand-crafted features. In this work we purposefully try to disregard domain- specific knowledge in favor of large-scale semi-supervised end-to-end learning. Our systems include several feature layers, with increasing abstraction level at [&hellip;]<\/p>\n","protected":false},"featured_media":195489,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr_hide_image_in_river":0,"footnotes":""},"research-area":[],"msr-video-type":[],"msr-locale":[268875],"msr-post-option":[],"msr-session-type":[],"msr-impact-theme":[],"msr-pillar":[],"msr-episode":[],"msr-research-theme":[],"class_list":["post-184570","msr-video","type-msr-video","status-publish","has-post-thumbnail","hentry","msr-locale-en_us"],"msr_download_urls":"","msr_external_url":"https:\/\/youtu.be\/gskjZ90ma94","msr_secondary_video_url":"","msr_video_file":"","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video\/184570","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-video"}],"version-history":[{"count":0,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video\/184570\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media\/195489"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=184570"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=184570"},{"taxonomy":"msr-video-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video-type?post=184570"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=184570"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=184570"},{"taxonomy":"msr-session-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-session-type?post=184570"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=184570"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=184570"},{"taxonomy":"msr-episode","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-episode?post=184570"},{"taxonomy":"msr-research-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-theme?post=184570"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}