{"id":164378,"date":"2013-05-01T00:00:00","date_gmt":"2013-05-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/using-a-knowledge-graph-and-query-click-logs-for-unsupervised-learning-of-relation-detection\/"},"modified":"2018-10-16T20:16:11","modified_gmt":"2018-10-17T03:16:11","slug":"using-a-knowledge-graph-and-query-click-logs-for-unsupervised-learning-of-relation-detection","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/using-a-knowledge-graph-and-query-click-logs-for-unsupervised-learning-of-relation-detection\/","title":{"rendered":"Using a Knowledge Graph and Query Click Logs for Unsupervised Learning of Relation Detection"},"content":{"rendered":"<div class=\"asset-content\">\n<p>We present unsupervised methods for training relation detection models from the semantic knowledge graphs of the semantic web. The detected relations are used to synthetically generate natural language spoken queries against a back-end knowledge base. For each relation, we leverage the complete set of entities that are connected to each other in the graph with the specific relation, and search these pairs on the web. We use the snippets that the search engine returns to create examples that can be used as the training data for each relation. We further refine the annotations of these examples using the knowledge graph itself and a bootstrap approach. Furthermore, we use the URLs returned for the pair by the search engine to mine additional examples from the search engine query click logs. In our experiments, we show that, we can achieve relation detection models that perform 59.9% macro F-measure on the relations that are in the knowledge graph without any  manual labeling, resulting in a comparable performance with supervised training.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>We present unsupervised methods for training relation detection models from the semantic knowledge graphs of the semantic web. The detected relations are used to synthetically generate natural language spoken queries against a back-end knowledge base. For each relation, we leverage the complete set of entities that are connected to each other in the graph with [&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":"lheck"},{"type":"user_nicename","value":"gokhant"},{"type":"user_nicename","value":"dilekha"}],"msr_publishername":"IEEE International Conference on Acoustics, Speech, and Signal Processing 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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. 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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