Open Access
BIO Web Conf.
Volume 8, 2017
2016 International Conference on Medicine Sciences and Bioengineering (ICMSB2016)
Article Number 01039
Number of page(s) 6
Section Session I: Medicine
Published online 11 January 2017
  • Hasegawa T, Sekine S, Grishman R. Discovering relations among named entities from large corpora[C]//Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics. Association for Computational Linguistics, 2004: 415. [Google Scholar]
  • Deng B, Fan X, Yang L. Entity relation extraction method using semantic pattern[J]. Jisuanji Gongcheng/ Computer Engineering, 2007, 33(10): 212–214. [Google Scholar]
  • Tikk D, Thomas P, Palaga P, et al. A comprehensive benchmark of kernel methods to extract protein–protein interactions from literature[J]. PLoS Comput Biol, 2010, 6(7): e1000837. [CrossRef] [Google Scholar]
  • Wishart D S, Knox C, Guo A C, et al. DrugBank: a comprehensive resource for in silico drug discovery and exploration[J]. Nucleic acids research, 2006, 34(suppl 1): D668–D672. [CrossRef] [Google Scholar]
  • Lafferty J., McCallum A., Pereira F. (2001). “Conditional random fields: Probabilistic models for segmenting and labeling sequence data”. Proc. 18th International Conf. on Machine Learning. Morgan Kaufmann. pp. 282–289. [Google Scholar]
  • He X.; Zemel R.S.; Carreira-Perpinñán M.A. (2004). “Multiscale conditional random fields for image labeling”. IEEE Computer Society. CiteSeerX: [Google Scholar]
  • Sha F., Pereira F. (2003). “shallow parsing with conditional random fields” [Google Scholar]
  • Settles B. (2004). “Biomedical named entity recognition using conditional random fields and rich feature sets”. Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications. pp. 104–107. [Google Scholar]
  • Tsuruoka Y, Tsujii J. Boosting precision and recall of dictionary-based protein name recognition[C]//Proceedings of the ACL 2003 workshop on Natural language processing in biomedicine-Volume 13. Association for Computational Linguistics, 2003: 41–48. [Google Scholar]
  • Wutao. Lin Research of medical information extraction based on multi label 2015[D]. CRF Wuhan: Wuhan University, 2015 [Google Scholar]
  • Toutanova Kristina, Klein Dan, Manning Christopher, and Si nger. 2003. Feature-Rich Part-of-Speech Tagging with a Cyclic Dependency Network. In Proceedings of HLT-NAACL 2003, pp. 252–259. [Google Scholar]
  • Chen Yoram D, Manning C D. A Fast and Accurate Dependency Parser using Neural Networks[C]// EMNLP. 2014: 740–750. [Google Scholar]
  • Zhu M, Zhang Y, Chen W, et al. Fast and Accurate Shift-Reduce Constituent Parsing[C]//ACL (1). 2013: 434–443. [Google Scholar]

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