{"id":168045,"date":"2014-12-01T00:00:00","date_gmt":"2014-12-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/personal-knowledge-graph-population-from-user-utterances-in-conversational-understanding\/"},"modified":"2018-10-16T20:05:49","modified_gmt":"2018-10-17T03:05:49","slug":"personal-knowledge-graph-population-from-user-utterances-in-conversational-understanding","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/personal-knowledge-graph-population-from-user-utterances-in-conversational-understanding\/","title":{"rendered":"Personal Knowledge Graph Population from User Utterances in Conversational Understanding"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Knowledge graphs provide a powerful representation of entities and the relationships between them, but automatically constructing such graphs from spoken language utterances presents the novelty and numerous challenges. In this paper, we introduce a statistical language understanding approach to automatically construct personal (user-centric) knowledge graphs in conversational dialogs. Such information has the potential to better understand the users\u2019 requests, fulfilling them, and enabling other technologies such as developing better inferences or proactive interactions. Knowledge encoded in semantic graphs such as Freebase has been shown to benefit semantic parsing and interpretation of natural language utterances. Hence, as a first step, we exploit the personal factual relation triples from Freebase to mine natural language snippets with a search engine, and the resulting snippets containing pairs of related entities to create the training data. This data is then used to build three key language understanding components: (1) <em>Personal Assertion Classification<\/em> identifies the user utterances that are relevant with personal facts, e.g., <em>\u201cmy mother\u2019s name is Rosa\u201d<\/em>; (2) <em>Relation Detection<\/em> classifies the personal assertion utterance into one of the predefined relation classes, e.g., <em>\u201cparents \u201d<\/em>; and (3) <em>Slot Filling<\/em> labels the attributes or arguments of relations, e.g., <em>\u201cname(parents):Rosa\u201d<\/em>. Our experiments using the Microsoft conversational understanding system demonstrate the performance of this proposed approach on the population of personal knowledge graphs.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Knowledge graphs provide a powerful representation of entities and the relationships between them, but automatically constructing such graphs from spoken language utterances presents the novelty and numerous challenges. In this paper, we introduce a statistical language understanding approach to automatically construct personal (user-centric) knowledge graphs in conversational dialogs. Such information has the potential to better [&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":"IEEE - Institute of Electrical and Electronics Engineers","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":"\u00a9 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting\/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.","msr_conference_name":"","msr_doi":"","msr_arxiv_id":"","msr_s2_paper_id":"","msr_mag_id":"","msr_pubmed_id":"","msr_other_authors":"","msr_other_contributors":"","msr_speaker":"","msr_award":"","msr_affiliation":"","msr_institution":"","msr_host":"","msr_version":"","msr_duration":"","msr_original_fields_of_study":"","msr_release_tracker_id":"","msr_s2_match_type":"","msr_citation_count_updated":"","msr_published_date":"2014-12-01","msr_highlight_text":"","msr_notes":"","msr_longbiography":"","msr_publicationurl":"","msr_external_url":"","msr_secondary_video_url":"","msr_conference_url":"","msr_journal_url":"","msr_s2_pdf_url":"","msr_year":2014,"msr_citation_count":0,"msr_influential_citations":0,"msr_reference_count":0,"msr_s2_match_confidence":0,"msr_microsoftintellectualproperty":true,"msr_s2_open_access":false,"msr_s2_author_ids":[],"msr_pub_ids":[],"msr_hide_image_in_river":0,"footnotes":""},"msr-research-highlight":[],"research-area":[13556,13554],"msr-publication-type":[193716],"msr-publisher":[],"msr-focus-area":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-168045","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-artificial-intelligence","msr-research-area-human-computer-interaction","msr-locale-en_us"],"msr_publishername":"IEEE - Institute of Electrical and Electronics Engineers","msr_edition":"","msr_affiliation":"","msr_published_date":"2014-12-01","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"217300","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"file","title":"SLT2014-xiang.pdf","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2014\/12\/SLT2014-xiang.pdf","id":217300,"label_id":0}],"msr_related_uploader":"","msr_citation_count":0,"msr_citation_count_updated":"","msr_s2_paper_id":"","msr_influential_citations":0,"msr_reference_count":0,"msr_arxiv_id":"","msr_s2_author_ids":[],"msr_s2_open_access":false,"msr_s2_pdf_url":null,"msr_attachments":[{"id":217300,"url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2014\/12\/SLT2014-xiang.pdf"}],"msr-author-ordering":[{"type":"text","value":"Xiang Li","user_id":0,"rest_url":false},{"type":"user_nicename","value":"gokhant","user_id":31896,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=gokhant"},{"type":"user_nicename","value":"dilekha","user_id":31630,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=dilekha"},{"type":"text","value":"Qi Li","user_id":0,"rest_url":false}],"msr_impact_theme":[],"msr_research_lab":[],"msr_event":[],"msr_group":[],"msr_project":[171393,171150],"publication":[],"video":[],"msr-tool":[],"msr_publication_type":"inproceedings","related_content":{"projects":[{"ID":171393,"post_title":"Knowledge 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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