TY - CONF
T1 - Cross-media event extraction and recommendation
AU - Lu, Di
AU - Voss, Clare R.
AU - Tao, Fangbo
AU - Ren, Xiang
AU - Guan, Rachel
AU - Korolov, Rostyslav
AU - Zhang, Tongtao
AU - Wang, Dongang
AU - Li, Hongzhi
AU - Cassidy, Taylor
AU - Ji, Heng
AU - Chang, Shih Fu
AU - Han, Jiawei
AU - Wallace, William
AU - Hendler, James
AU - Si, Mei
AU - Kaplan, Lance
N1 - Funding Information:
This work was supported by the U.S. ARL NS-CTA No. W911NF-09-2-0053, DARPA Multimedia Seedling grant, DARPA DEFT No. FA8750-13-2-0041 and NSF CAREER Award IIS-1523198. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on.
Publisher Copyright:
© NAACL-HLT 2016 - 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Demonstrations Session. All rights reserved.
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
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M3 - Paper
AN - SCOPUS:85076747055
SP - 72
EP - 76
T2 - 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2016
Y2 - 12 June 2016 through 17 June 2016
ER -