{"id":183776,"date":"2005-12-07T00:00:00","date_gmt":"2009-10-31T13:01:26","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/better-k-best-parsing-hypergraphs-and-dynamic-programming\/"},"modified":"2016-09-09T09:51:27","modified_gmt":"2016-09-09T16:51:27","slug":"better-k-best-parsing-hypergraphs-and-dynamic-programming","status":"publish","type":"msr-video","link":"https:\/\/www.microsoft.com\/en-us\/research\/video\/better-k-best-parsing-hypergraphs-and-dynamic-programming\/","title":{"rendered":"Better k-best Parsing, Hypergraphs, and Dynamic Programming"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Joint work with David Chiang (University of Maryland)<\/p>\n<p>K-best parsing (and k-best processing in general) has become a popular technique in natural language processing. However, fast and exact k-best algorithms are largely unknown to the parsing community. In this work, we develop efficient algorithms for exact k-best derivation trees in the framework of directed monotonic hypergraphs. To demonstrate the efficiency, scalability  and accuracy of these algorithms, we present experiments on Bikel&#8217;s implementation of Collins&#8217; lexicalized PCFG model, and on Chiang&#8217;s CFG-based decoder for hierarchical phrase-based translation.  We show in particular how the improved output of our algorithms has the potential to improve results from parse reranking systems and other applications.<\/p>\n<p>These algorithms have been re-implemented by other researchers in the field, including Eugene Charniak for his n-best parser, Ryan McDonald and Microsoft Research for dependency parsers, and the USC\/ISI team for the CKY-based decoder in their syntax-based machine translation system. All of these experiments confirmed the findings in our work.<\/p>\n<p>Reference:<br \/>\nLiang Huang and David Chiang (2005). Better k-best Parsing.  Proceedings of the 9th International Workshop on Parsing Technologies (IWPT). http:\/\/www.cis.upenn.edu\/~lhuang3\/huang-iwpt.pdf<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Joint work with David Chiang (University of Maryland) K-best parsing (and k-best processing in general) has become a popular technique in natural language processing. However, fast and exact k-best algorithms are largely unknown to the parsing community. In this work, we develop efficient algorithms for exact k-best derivation trees in the framework of directed monotonic [&hellip;]<\/p>\n","protected":false},"featured_media":195188,"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-183776","msr-video","type-msr-video","status-publish","has-post-thumbnail","hentry","msr-locale-en_us"],"msr_download_urls":"","msr_external_url":"https:\/\/youtu.be\/kSlBxsNNOEw","msr_secondary_video_url":"","msr_video_file":"","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video\/183776","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\/183776\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media\/195188"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=183776"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=183776"},{"taxonomy":"msr-video-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video-type?post=183776"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=183776"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=183776"},{"taxonomy":"msr-session-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-session-type?post=183776"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=183776"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=183776"},{"taxonomy":"msr-episode","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-episode?post=183776"},{"taxonomy":"msr-research-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-theme?post=183776"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}