Advertisement

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

  • Huiwei ZhouEmail author
  • Yunlong Yang
  • Zhuang Liu
  • Zhe Liu
  • Yahui Men
Conference paper
  • 1.4k Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10565)

Abstract

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.

Keywords

CDR extraction CNN Word sequences Dependency structures 

Notes

Acknowledgements

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

References

  1. 1.
    Dogan, R.I., Murray, G.C., Névéol, A., Lu, Z.Y.: Understanding PubMed user search behavior through log analysis. Database (2009), doi: 10.1093/database/bap018
  2. 2.
    Wei, C.H., Peng, Y.F., Leaman, Ret al.: Overview of the biocreative v chemical disease relation (CDR) task. In: The Fifth BioCreative Challenge Evaluation Workshop, pp. 154–166 (2015)Google Scholar
  3. 3.
    Lowe, D.M., O’Boyle, N.M., Sayle, R.A.: Efficient chemical-disease identification and relationship extraction using Wikipedia to improve recall. Database (2016), doi: 10.1093/database/baw039
  4. 4.
    Xu, J., Wu, Y.H., Zhang, Y.Y., Wang, J.Q., Lee, H., Xu, H.: CD-REST: a system for extracting chemical-induced disease relation in literature. Database (2016), doi: 10.1093/database/baw036
  5. 5.
    Gu, J.H., Qian, L.H and Zhou, G.D.: Chemical-induced disease relation extraction with various linguistic features. Database (2016), doi: 10.1093/database/baw042
  6. 6.
    Pons, E., Becker, B.F.H., Akhondi, S.A., Afzal, Z., van Mulligen, E.M., Kors, J.A.: Extraction of chemical-induced diseases using prior knowledge and textual information. Database (2016), doi: 10.1093/database/baw046
  7. 7.
    Zhou, H.W., Deng, H.J., Chen, L., Yang, Y.L., Jia, C., Huang, D.G.: Exploiting syntactic and semantics information for chemical-disease relation extraction. Database (2016), doi: 10.1093/database/baw048
  8. 8.
    Gers, F.A., Schmidhuber, J.: Recurrent nets that time and count. In: Neural Networks: Como, vol. 3, pp. 189–194 (2000)Google Scholar
  9. 9.
    Nguyen, T.H., Grishman, R.: Relation extraction: perspective from convolutional neural networks. In: The NAACL Workshop on Vector Space Modeling for NLP, pp. 39–48 (2015)Google Scholar
  10. 10.
    Kalchbrenner, N., Grefenstette, R., Blunsom, P.: A convolutional neural network for modelling sentences. In: Proceeding of ACL, pp. 655–665 (2014)Google Scholar
  11. 11.
    Vu, N.T., Adel, H., Gupta, P., Schütze, H.: Combining recurrent and convolutional neural networks for relation classification. In: Proceedings of NAACL-HLT, pp. 534–539 (2016)Google Scholar
  12. 12.
    Zeng, D.J., Liu, K., Chen, Y.B., Zhao, J.: Distant supervision for relation extraction via piecewise convolutional neural networks. In: Proceedings of EMNLP, pp. 1753–1762 (2015)Google Scholar
  13. 13.
    Coletti, M.H., Bleich, H.L.: Medical subject headings used to search the biomedical literature. J. Am. Med. Inform. Assoc. 8, 317–323 (2011)CrossRefGoogle Scholar
  14. 14.
    Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Proceedings of NIPS, pp. 3111–3119 (2013)Google Scholar
  15. 15.
    Xie, R.B., Liu, Z.Y., Sun, M.S.: Representation learning of knowledge graphs with hierarchical types. In: Proceedings of AAAI, pp. 2965–2971 (2016)Google Scholar

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

Personalised recommendations