The 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.
CITATION STYLE
Lu, D., Voss, C. R., Tao, F., Ren, X., Guan, R., Korolov, R., … Kaplan, L. (2016). Cross-media event extraction and recommendation. In NAACL-HLT 2016 - 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Demonstrations Session (pp. 72–76). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/n16-3015
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