{"id":167727,"date":"2014-08-01T00:00:00","date_gmt":"2014-08-01T00:00:00","guid":{"rendered":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/msr-research-item\/bringing-structure-to-text-mining-phrases-entity-concepts-topics-and-hierarchies\/"},"modified":"2018-10-16T22:30:59","modified_gmt":"2018-10-17T05:30:59","slug":"bringing-structure-to-text-mining-phrases-entity-concepts-topics-and-hierarchies","status":"publish","type":"msr-research-item","link":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/publication\/bringing-structure-to-text-mining-phrases-entity-concepts-topics-and-hierarchies\/","title":{"rendered":"Bringing Structure to Text: Mining Phrases, Entity Concepts, Topics, and Hierarchies"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Mining phrases, entity concepts, topics, and hierarchies from massive text corpus is an essential problem in the age of big data. Text data in electronic forms are ubiquitous, ranging from scientific articles to social networks, enterprise logs, news articles, social media and general web pages. It is highly desirable but challenging to bring structure to unstructured text data, uncover underlying hierarchies, relationships, patterns and trends, and gain knowledge from such data.<\/p>\n<p>In this tutorial, we provide a comprehensive survey on the state-of-the art of data-driven methods that automatically mine phrases, extract and infer latent structures from text corpus, and construct multi-granularity topical groupings and hierarchies of the underlying themes. We study their principles, methodologies, algorithms and applications using several real datasets including research papers and news articles and demonstrate how these methods work and how the uncovered latent entity structures may help text understanding, knowledge discovery and management.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Mining phrases, entity concepts, topics, and hierarchies from massive text corpus is an essential problem in the age of big data. Text data in electronic forms are ubiquitous, ranging from scientific articles to social networks, enterprise logs, news articles, social media and general web pages. It is highly desirable but challenging to bring structure to [&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":"ACM \u2013 Association for Computing Machinery","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"KDD'14","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 ACM. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. 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