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dc.contributor.authorLu, Di
dc.contributor.authorSi, Mei
dc.contributor.authorVoss, Claire R.
dc.contributor.authorTao, Fangbo
dc.contributor.authorren, Xiang
dc.contributor.authorGuan, Rachel
dc.contributor.authorKorolov, Rostyslav
dc.contributor.authorJi, Heng
dc.contributor.authorHendler, James A.
dc.identifier.citationDi Lu, Clare Voss, Fangbo Tao, Xiang Ren, Rachel Guan, Rostyslav Korolov, Tongtao Zhang, Dongang Wang, Hongzhi Li, Taylor Cassidy, Heng Ji, Shih-fu Chang, Jiawei Han, William Wallace, James Hendler, Mei Si, and Lance Kaplan. 2016. Cross-media Event Extraction and Recommendation. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations, pages 72–76, San Diego, California. Association for Computational Linguistics.en_US
dc.description.abstractThe sheer volume of unstructured multimedia data (e.g., texts, images, videos) posted on the Web during events of general interest is overwhelming and difficult to distill if seeking information relevant to a particular concern. We have developed a comprehensive system that searches, identifies, organizes and summarizes complex events from multiple data modalities. It also recommends events related to the user’s ongoing search based on previously selected attribute values and dimensions of events being viewed. In this paper we briefly present the algorithms of each component and demonstrate the system’s capabilities.en_US
dc.publisherAssociation for Computational Linguisticsen_US
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.titleCross-media Event Extraction and Recommendationen_US

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Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 United States