{"id":163457,"date":"2011-08-01T00:00:00","date_gmt":"2011-08-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/learning-weighted-entity-lists-from-web-click-logs-for-spoken-language-understanding\/"},"modified":"2018-10-16T22:03:49","modified_gmt":"2018-10-17T05:03:49","slug":"learning-weighted-entity-lists-from-web-click-logs-for-spoken-language-understanding","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/learning-weighted-entity-lists-from-web-click-logs-for-spoken-language-understanding\/","title":{"rendered":"Learning Weighted Entity Lists from Web Click Logs for Spoken Language Understanding"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Named entity lists provide important features for language understanding, but typical lists can contain many ambiguous or incorrect phrases. We present an approach for automatically learning weighted entity lists by mining user clicks from web search logs. The approach significantly outperforms multiple baseline approaches and the weighted lists improve spoken language understanding tasks such as domain detection and slot filling. Our methods are general and can be easily applied to large quantities of entities, across any number of lists.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Named entity lists provide important features for language understanding, but typical lists can contain many ambiguous or incorrect phrases. We present an approach for automatically learning weighted entity lists by mining user clicks from web search logs. The approach significantly outperforms multiple baseline approaches and the weighted lists improve spoken language understanding tasks 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":null,"msr_publishername":"Annual Conference of the International Speech Communication Association 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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. 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