{"id":165092,"date":"2013-08-01T00:00:00","date_gmt":"2013-08-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/a-weakly-supervised-approach-for-discovering-new-user-intents-from-search-query-logs\/"},"modified":"2018-10-16T21:29:08","modified_gmt":"2018-10-17T04:29:08","slug":"a-weakly-supervised-approach-for-discovering-new-user-intents-from-search-query-logs","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/a-weakly-supervised-approach-for-discovering-new-user-intents-from-search-query-logs\/","title":{"rendered":"A Weakly-Supervised Approach for Discovering New User Intents from Search Query Logs"},"content":{"rendered":"<div class=\"asset-content\">\n<p>State-of-the art spoken language understanding models that automatically capture user intents in human to machine dialogs are trained with manually annotated data, which is cumbersome and time-consuming to prepare. For bootstrapping the learning algorithm that detects relations in natural language queries to a conversational system, one can rely on publicly available knowledge graphs, such as Freebase, and mine corresponding data from the web. In this paper, we present an unsupervised approach to discover new user intents using a novel Bayesian hierarchical graphical model. Our model employs search query click logs to enrich the information extracted from bootstrapped models. We use the clicked URLs as implicit supervision and extend the knowledge graph based on the relational information discovered from this model. The posteriors from the graphical model relate the newly discovered intents with the search queries. These queries are then used as additional training examples to complement the bootstrapped relation detection models. The experimental results demonstrate the effectiveness of this approach, showing extended coverage to new intents without impacting the known intents.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>State-of-the art spoken language understanding models that automatically capture user intents in human to machine dialogs are trained with manually annotated data, which is cumbersome and time-consuming to prepare. For bootstrapping the learning algorithm that detects relations in natural language queries to a conversational system, one can rely on publicly available knowledge graphs, such as [&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":"dilekha"},{"type":"user_nicename","value":"aslicel"},{"type":"user_nicename","value":"lheck"},{"type":"user_nicename","value":"gokhant"}],"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"","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":"Annual Conference 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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