Adaptive System for Handling Variety in Big Text-Reference-Cited by-同舟云学术

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Adaptive System for Handling Variety in Big Text

Published:2017-10-24 Issue: Volume: Page:305-313
ISSN:2367-3370
Container-title:Intelligent Communication and Computational Technologies
language:
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Author:

Pathak Shantanu,Rajeshwar Rao D.

Publisher

Springer Singapore

Link

http://link.springer.com/content/pdf/10.1007/978-981-10-5523-2_28

Reference36 articles.

1. S. Kaisler, F. Armour, J. A. Espinosa, and W. Money, “Big data: Issues and challenges moving forward,” in System Sciences (HICSS), 2013 46th Hawaii International Conference on. IEEE, 2013, pp. 995–1004.

2. B. Shneiderman and C. Plaisant, “Sharpening analytic focus to cope with big data volume and variety,” Computer Graphics and Applications, IEEE, vol. 35, no. 3, pp. 10–14, 2015.

3. A. Sarker and G. Gonzalez, “Portable automatic text classification for adverse drug reaction detection via multi-corpus training,” Journal of biomedical informatics, vol. 53, pp. 196–207, 2015.

4. Y. Zheng, W. Han, and C. Zhu, “A novel feature selection method based on category distribution and phrase attributes,” in Trustworthy Computing and Services. Springer, 2014, pp. 25–32.

5. C.-P. Wei, C.-S. Yang, C.-H. Lee, H. Shi, and C. C. Yang, “Exploiting poly-lingual documents for improving text categorization effectiveness,” Decision Support Systems, vol. 57, pp. 64–76, 2014.

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