Interpreting the user intent in search queries is a key task in query understanding. Query intent classification has been widely studied. In this paper, we go one step further to understand the query from the view of head-modifier analysis. For example, given the query \u201cpopular iphone 5 smart cover,\u201d instead of using coarse-grained semantic classes (e.g., find electronic product), we interpret that \u201csmart cover\u201d is the head or the intent of the query and \u201ciphone 5\u201d is its modifier. Query head-modifier detection can help search engines to obtain particularly relevant content, which is also important for applications such as ads matching and query recommendation. We introduce an unsupervised semantic approach for query head-modifier detection. First, we mine a large number of instance level head-modifier pairs from search log. Then, we develop a conceptualization mechanism to generalize the instance level pairs to concept level. Finally, we derive weighted concept patterns that are concise, accurate, and have strong generalization power in head-modifier detection. The developed mechanism has been used in production for search relevance and ads matching. We use extensive experiment results to demonstrate the effectiveness of our approach.<\/p>\n
Thanks for your interests in this paper. Please also pay attentions to our ACL 2016 short text understanding tutorial: Understanding Short Texts \u2013 ACL 2016 Tutorial<\/strong> (opens in new tab)<\/span><\/a>, presented by Zhongyuan Wang<\/b> (opens in new tab)<\/span><\/a>.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":" Interpreting the user intent in search queries is a key task in query understanding. Query intent classification has been widely studied. In this paper, we go one step further to understand the query from the view of head-modifier analysis. For example, given the query \u201cpopular iphone 5 smart cover,\u201d instead of using coarse-grained semantic classes […]<\/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":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13556,13545,13555],"msr-publication-type":[193715],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-295037","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-artificial-intelligence","msr-research-area-human-language-technologies","msr-research-area-search-information-retrieval","msr-locale-en_us"],"msr_publishername":"","msr_edition":"","msr_affiliation":"","msr_published_date":"2016-09-01","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"TKDD","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":"295040","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"file","title":"head-modifier-tkdd","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/09\/head-modifier-TKDD.pdf","id":295040,"label_id":0}],"msr_related_uploader":"","msr_attachments":[],"msr-author-ordering":[{"type":"user_nicename","value":"zhowang","user_id":35131,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=zhowang"},{"type":"text","value":"Fang Wang","user_id":0,"rest_url":false},{"type":"text","value":"Haixun Wang","user_id":0,"rest_url":false},{"type":"text","value":"Zhirui Hu","user_id":0,"rest_url":false},{"type":"user_nicename","value":"junyan","user_id":32448,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=junyan"},{"type":"text","value":"Fangtao Li","user_id":0,"rest_url":false},{"type":"text","value":"Ji-Rong Wen","user_id":0,"rest_url":false},{"type":"text","value":"Zhoujun Li","user_id":0,"rest_url":false}],"msr_impact_theme":[],"msr_research_lab":[199560],"msr_event":[],"msr_group":[],"msr_project":[170584,293774],"publication":[],"video":[],"download":[],"msr_publication_type":"article","related_content":{"projects":[{"ID":170584,"post_title":"Probase","post_name":"probase","post_type":"msr-project","post_date":"2010-10-29 03:13:04","post_modified":"2017-06-05 10:40:21","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/probase\/","post_excerpt":"The goal of Probase is to make machines \u201caware\u201d of the mental world of human beings, so that\u00a0machines can better understand human communication. 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