Computer Science ›› 2020, Vol. 47 ›› Issue (3): 192-199.doi: 10.11896/jsjkx.190300137

• Artificial Intelligence • Previous Articles     Next Articles

Research and Development of Multi-label Generation Based on Deep Learning

LIU Xiao-ling,LIU Bai-song,WANG Yang-yang,TANG Hao   

  1. (Faculty of Information Science and Technology, Ningbo University, Ningbo, Zhejiang 315211, China)
  • Received:2019-03-26 Online:2020-03-15 Published:2020-03-30
  • About author:LIU Xiao-ling, born in 1994,postgra-duate.Her main research interests include natural language processing and data mining. LIU Bai-song,born in 1971,Ph.D,researcher,Ph.D supervisor,is member of China Computer Federation.His main research interests include natural language processing,big data and artificial intelligence.
  • Supported by:
    This work was supported by the National Social Science Foundation of China (15FTQ002).

Abstract: In the era of big data,data show the characteristics of high dimension,large amount and rapid growth.Efficiently discovering knowledge from these data is a research focus.Multi-label has been proposed for ambiguous objects in reality,and is widely used in data intelligent processing.In recent years,Multi-label generation receives widespread attention due to the excellent performance of deep learning.The latest research results were summarized from five categories and were further compared and analyzed from the aspects of data,relationship types,application scene,adaptability and experimental performance.Finally,the challenges of multi-label generation were discussed,followed with the prospects for future work.

Key words: Deep learning, Label correlation, Multi-label learning, Neural networks

CLC Number: 

  • TP391
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