Du Yongping

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Personal profile

Gender: Female

Degrees: Ph.D.

Title: Professor

E-mail: ypdu@bjut.edu.cn

Research Areas: Natural Language Processing, Information Retrieval, Information Extraction, Question Answering

Publications

  1. Yongping Du, Changqing Yao, Shuhua Huo, Jingxuan Liu. A New Item-based Deep Network Structure using a Restricted Boltzmann Machine for Collaborative Filtering. Frontiers of Information Technology & Electronic Engineering. 2017. Vol.18 No.5:658-666. (SCI)

  2. Yongping Du, Jingxuan Liu, Weimao Ke, Xuemei Gong. Hierarchy construction and text classification based on the relaxation strategy and least information model. Expert Systems with Applications, Volume 100, 2018, Pages 157-164. (SCI)

  3. Yongping Du, Bingbing Pei, Xiaozheng Zhao, Junzhong Ji. Hierarchical Multi-layer Transfer Learning Model for Biomedical Question Answering. IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM 2018). Volume 1. pp: 362-367. Madrid, Spain. 2018 (CCF Category B Conference )

  4. Yongping Du, Yunpeng Pan, Chencheng Wang, Junzhong Ji. Biomedical semantic indexing by deep neural network with multi-task learning. BMC Bioinformatics. Volume: 19. Article Number: 502. Supplement: 20. 2018. (SCI)

  5. Yongping Du, Chencheng Wang, Yanlei Qiao, Dongyue Zhao, Wenyang Guo. A Geographical Location Prediction Method Based On Continuous Time Series Markov Model. 2018. PLOS ONE. Volume: 13, Issue: 11. (SCI)

  6. Yongping Du, Wenyang Guo, Bingbing Pei.Hierarchical Question-Aware Context Learning with Augmented Data for Biomedical Question Answering. IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM). Sandiego, America. 2019. (CCF Category B Conference)

 

 

Personal Statement

Dr. Du graduated from Fudan University in 2005. Her current research interests are data/text/web mining, information retrieval and information extraction.


Question Answering is an important task for deep natural language understanding and cognitive intelligence improvement of machine. Our work focuses on the research of fragmented multi-evidence fusion and knowledge reasoning to improve the natural language cognitive ability of the machine.