{"id":163444,"date":"2011-05-01T00:00:00","date_gmt":"2011-05-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/exploiting-query-click-logs-for-utterance-domain-detection-in-spoken-language-understanding\/"},"modified":"2018-10-16T22:01:37","modified_gmt":"2018-10-17T05:01:37","slug":"exploiting-query-click-logs-for-utterance-domain-detection-in-spoken-language-understanding","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/exploiting-query-click-logs-for-utterance-domain-detection-in-spoken-language-understanding\/","title":{"rendered":"Exploiting Query Click Logs for Utterance Domain Detection in Spoken Language Understanding"},"content":{"rendered":"<div class=\"asset-content\">\n<p>In this paper, we describe methods to exploit search queries mined from search engine query logs to improve domain detection in spoken language understanding. We propose extending the label propagation algorithm, a graph-based semi-supervised learning approach, to incorporate noisy domain information estimated from search engine links the users click following their queries. The main contributions of our work are the use of search query logs for domain classification, integration of noisy supervision into the semi-supervised label propagation algorithm, and sampling of high-quality query click data by mining query logs and using classification confidence scores. We show that most semi-supervised learning methods we experimented with improve the performance of the supervised training, and the biggest improvement is achieved by label propagation that uses noisy supervision. We reduce the to error rate of domain detection by 20% relative, from 6.2% to 5.0%.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this paper, we describe methods to exploit search queries mined from search engine query logs to improve domain detection in spoken language understanding. We propose extending the label propagation algorithm, a graph-based semi-supervised learning approach, to incorporate noisy domain information estimated from search engine links the users click following their queries. The main contributions [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":[{"type":"user_nicename","value":"gokhant"},{"type":"user_nicename","value":"lheck"},{"type":"user_nicename","value":"dilekha"}],"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"Proceedings of the ICASSP","msr_chapter":"","msr_edition":"Proceedings of the ICASSP","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"Proceedings of the 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Graphs and Linked Big Data Resources for Conversational Understanding","post_name":"knowledge-graphs-and-linked-big-data-resources-for-conversational-understanding","post_type":"msr-project","post_date":"2014-08-13 20:10:32","post_modified":"2017-06-19 11:05:46","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/knowledge-graphs-and-linked-big-data-resources-for-conversational-understanding\/","post_excerpt":"Interspeech 2014 Tutorial Web Page State-of-the-art statistical spoken language processing typically requires significant manual effort to construct domain-specific schemas (ontologies) as well as manual effort to annotate training data against these schemas. At the same time, a recent surge of activity and progress on semantic web-related concepts from the large search-engine companies represents a potential alternative to the manually intensive design of spoken language processing systems. Standards such as schema.org have been established for schemas&hellip;","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/171393"}]}},{"ID":171150,"post_title":"Spoken Language Understanding","post_name":"spoken-language-understanding","post_type":"msr-project","post_date":"2013-05-01 11:46:32","post_modified":"2019-08-19 14:48:51","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/spoken-language-understanding\/","post_excerpt":"Spoken language understanding (SLU) is an emerging field in between the areas of speech processing and natural language processing. The term spoken language understanding has largely been coined for targeted understanding of human speech directed at machines. This project covers our research on SLU tasks such as domain detection, intent determination, and slot filling, using data-driven methods. Projects Deeper Understanding: Moving\u00a0beyond shallow targeted understanding towards building domain independent SLU models. 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