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Volumn 24-28-October-2016, Issue , 2016, Pages 1129-1138

Characterizing diseases from unstructured text: A vocabulary driven Word2vec approach

Author keywords

Dis2Vec; Disease characterization; Domain specific word embeddings; HealthMap; Word2vec

Indexed keywords

TAXONOMIES;

EID: 84996497464     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2983323.2983362     Document Type: Conference Paper
Times cited : (27)

References (22)
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    • Baroni, M.1    Dinu, G.2    Kruszewski, G.3
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    • A unified architecture for natural language processing: Deep neural networks with multitask learning
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    • Collobert, R.1    Weston, J.2
  • 7
    • 39649091877 scopus 로고    scopus 로고
    • Healthmap: Global infectious disease monitoring through automated classification and visualization of internet media reports
    • C. C. Freifeld, K. D. Mandl, B. Y. Reis, and J. S. Brownstein. Healthmap: global infectious disease monitoring through automated classification and visualization of internet media reports. Journal of the American Medical Informatics Association, 15(2):150-157, 2008.
    • (2008) Journal of the American Medical Informatics Association , vol.15 , Issue.2 , pp. 150-157
    • Freifeld, C.C.1    Mandl, K.D.2    Reis, B.Y.3    Brownstein, J.S.4
  • 11
    • 84943775110 scopus 로고    scopus 로고
    • Improving distributional similarity with lessons learned from word embeddings
    • O. Levy, Y. Goldberg, and I. Dagan. Improving distributional similarity with lessons learned from word embeddings. TACL, 3:211-225, 2015.
    • (2015) TACL , vol.3 , pp. 211-225
    • Levy, O.1    Goldberg, Y.2    Dagan, I.3
  • 12
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    • Efficient estimation of word representations in vector space
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    • 'beating the news' with embers: Forecasting civil unrest using open source indicators
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    • N. Ramakrishnan, P. Butler, S. Muthiah, et al. 'beating the news' with embers: Forecasting civil unrest using open source indicators. In Proceedings of the 20th ACM SIGKDD, pages 1799-1808, New York, NY, USA, 2014. ACM.
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    • Dynamic poisson autoregression for influenza-like-illness case count prediction
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    • Z. Wang, P. Chakraborty, N. Ramakrishnan, et al. Dynamic poisson autoregression for influenza-like-illness case count prediction. In Proceedings of the 21th ACM SIGKDD, pages 1285-1294. ACM, 08 2015.
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.