{"id":154780,"date":"2003-01-01T00:00:00","date_gmt":"2003-01-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/combination-of-cfg-and-n-gram-modeling-in-semantic-grammar-learning\/"},"modified":"2018-10-16T21:33:52","modified_gmt":"2018-10-17T04:33:52","slug":"combination-of-cfg-and-n-gram-modeling-in-semantic-grammar-learning","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/combination-of-cfg-and-n-gram-modeling-in-semantic-grammar-learning\/","title":{"rendered":"Combination of CFG and N-gram Modeling in Semantic Grammar Learning"},"content":{"rendered":"<div class=\"asset-content\">\n<p>SGStudio is a grammar authoring tool that eases semantic grammar development. It is capable of integrating different information sources and learning from annotated examples to induct CFG rules. In this paper, we investigate a modification to its underlying model by replacing CFG rules with n-gram statistical models. The new model is a composite of HMM and CFG. The advantages of the new model include its built-in robust feature and its scalability to an n-gram classifier when the understanding does not involve slot filling. We devised a decoder for the model. Preliminary results show that the new model achieved 32% error reduction in high resolution understanding.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>SGStudio is a grammar authoring tool that eases semantic grammar development. It is capable of integrating different information sources and learning from annotated examples to induct CFG rules. In this paper, we investigate a modification to its underlying model by replacing CFG rules with n-gram statistical models. The new model is a composite of HMM [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13545],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-154780","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-human-language-technologies","msr-locale-en_us"],"msr_publishername":"International Speech Communication Association","msr_edition":"Eurospeech 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