Integrating Word Sequences and Dependency Structures for Chemical-Disease Relation Extraction

  • Huiwei ZhouEmail author
  • Yunlong Yang
  • Zhuang Liu
  • Zhe Liu
  • Yahui Men
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10565)


Understanding chemical-disease relations (CDR) from biomedical literature is important for biomedical research and chemical discovery. This paper uses a k-max pooling convolutional neural network (CNN) to exploit word sequences and dependency structures for CDR extraction. Furthermore, an effective weighted context method is proposed to capture semantic information of word sequences. Our system extracts both intra- and inter-sentence level chemical-disease relations, which are merged as the final CDR. Experiments on the BioCreative V CDR dataset show that both word sequences and dependency structures are effective for CDR extraction, and their integration could further improve the extraction performance.


CDR extraction CNN Word sequences Dependency structures 



This research is supported by Natural Science Foundation of China (No. 61272375).


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Huiwei Zhou
    • 1
    Email author
  • Yunlong Yang
    • 1
  • Zhuang Liu
    • 1
  • Zhe Liu
    • 2
  • Yahui Men
    • 2
  1. 1.School of Computer Science and TechnologyDalian University of TechnologyDalianChina
  2. 2.School of Life Science and MedicineDalian University of TechnologyDalianChina

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