{"id":156813,"date":"2008-01-01T00:00:00","date_gmt":"2008-01-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/scalable-adhoc-entity-extraction-from-text-collections\/"},"modified":"2018-10-16T21:12:39","modified_gmt":"2018-10-17T04:12:39","slug":"scalable-adhoc-entity-extraction-from-text-collections","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/scalable-adhoc-entity-extraction-from-text-collections\/","title":{"rendered":"Scalable Adhoc Entity Extraction from Text Collections"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Supporting entity extraction from large document collections is important for enabling a variety of important data analysis tasks. In this paper, we introduce the ad -hoc&#8221; entity extraction task where entities of interest are constrained to be from a list of entities that is specific to the task. In such scenarios, traditional entity extraction techniques that process all the documents for each ad-hoc entity extraction task can be significantly expensive. In this paper, we propose an efficient approach that leverages the inverted index on the documents to identify the subset of documents relevant to the task and processes only those documents. We demonstrate the efficiency of our techniques on real datasets.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Supporting entity extraction from large document collections is important for enabling a variety of important data analysis tasks. In this paper, we introduce the ad -hoc&#8221; entity extraction task where entities of interest are constrained to be from a list of entities that is specific to the task. In such scenarios, traditional entity extraction techniques [&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":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13555],"msr-publication-type":[193716],"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-156813","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-search-information-retrieval","msr-locale-en_us"],"msr_publishername":"","msr_edition":"VLDB Conference","msr_affiliation":"","msr_published_date":"2008-01-01","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"VLDB Conference","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":"208301","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"file","title":"vldb2008_lookup_driven_extraction.pdf","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/vldb2008_lookup_driven_extraction.pdf","id":208301,"label_id":0}],"msr_related_uploader":"","msr_attachments":[{"id":208301,"url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/vldb2008_lookup_driven_extraction.pdf"}],"msr-author-ordering":[{"type":"user_nicename","value":"sagrawal","user_id":33490,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=sagrawal"},{"type":"user_nicename","value":"kaushik","user_id":32503,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=kaushik"},{"type":"user_nicename","value":"surajitc","user_id":33764,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=surajitc"},{"type":"user_nicename","value":"vganti","user_id":34554,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=vganti"}],"msr_impact_theme":[],"msr_research_lab":[],"msr_event":[],"msr_group":[957177],"msr_project":[169514],"publication":[],"video":[],"download":[],"msr_publication_type":"inproceedings","related_content":{"projects":[{"ID":169514,"post_title":"Data Exploration","post_name":"data-exploration","post_type":"msr-project","post_date":"2004-06-08 15:56:40","post_modified":"2017-06-06 10:57:58","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/data-exploration\/","post_excerpt":"This is a project area rather than a specific project. This project area focuses on novel ways to query, browse, extract, explore, mine and manage various kinds of data residing within the enterprise and on the web: structured data in relational databases, tabular data embedded in web pages, enterprise documents and spreadsheets as well as unstructured data in query logs, text documents and social media. 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