Disease named entity recognition by combining conditional random fields and bidirectional recurrent neural networks

Database

Volumn 2016, Issue , 2016, Pages 1-8

  Wei, Qikang ^a   Chen, Tao ^a   Xu, Ruifeng ^a   He, Yulan ^b   Gui, Lin ^a  

^a HARBIN INSTITUTE OF TECHNOLOGY   (China)

^b ASTON UNIVERSITY   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 85016100938     PISSN: 17580463     EISSN: None     Source Type: Journal    
DOI: 10.1093/database/baw140     Document Type: Article

References (23)

1. Wei, C.H., Peng, Y., Leaman, R. et al. (2015) Overview of the BioCreative V chemical disease relation (CDR) task. In: Proceedings of the Fifth BioCreative Challenge Evaluation Workshop. Sevilla, Spain.
2. Leaman, R., Islamaj Dogan, R. and Lu, Z. (2013) DNorm: disease name normalization with pairwise learning to rank. Bioinformatics, 29, 2909-2917.
3. Hettne, K.M., Stierum, R.H., Schuemie, M.J. et al. (2009) A dictionary to identify small molecules and drugs in free text. Bioinformatics, 25, 2983-2991.
4. Rocktäschel, T., Weidlich, M. and Leser, U. (2012) ChemSpot: a hybrid system for chemical named entity recognition. Bioinformatics, 28, 1633-1640.
5. Klinger, R., Kolářik, C., Fluck, J. et al. (2008) Detection of IUPAC and IUPAC-like chemical names. Bioinformatics, 24, i268-i276.
6. Leaman, R. and Gonzalez, G. (2008, January). BANNER: an executable survey of advances in biomedical named entity recognition. In: Pacific Symposium on Biocomputing, Vol.13, pp. 652-663.
7. Leaman, R., Wei, C.-H. and Lu, Z. (2015) tmChem: a high performance approach for chemical named entity recognition and normalization. Journal of cheminformatics., 20157(Suppl 1): S3.
8. Chiu, J.P. and Nichols, E. (2016). Named entity recognition with bidirectional LSTM-CNNs. Transactions of the Association for Computational Linguistics, 4, 357-370.
9. dos Santos, C.N., Guimaraes, V., Niterói, R.J. et al. (2015) Boosting named entity recognition with neural character embeddings. In Proceedings of NEWS 2015 The Fifth Named Entities Workshop, p25.
10. Lample, G., Ballesteros, M., Subramanian, S. et al. (2016) Neural architectures for named entity recognition. In: Proceedings of the HLT-NAACL 2016 Workshop, pp. 260-270.
11. Irsoy, O. and Cardie, C. (2014) Opinion mining with deep recurrent neural networks. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), October 2014, pp. 720-728.
12. Lu, Z. (2010) PubMed and beyond: a survey of web tools for searching biomedical literature. Database, 2011, baq036.
13. Li, J., Sun, Y., Johnson, R.J. et al. (2015) Annotating chemicals, diseases, and their interactions in biomedical literature. In: Proceedings of the Fifth BioCreative Challenge Evaluation Workshop. Sevilla, Spain.
14. Tsuruoka, Y., Tateishi, Y., Kim, J.D. et al. (2005) Developing a robust part-of-speech tagger for biomedical text. Adv. Inform., LNCS 3746, pp. 382-392.
15. Settles, B. (2004) Biomedical named entity recognition using conditional random fields and rich feature sets. In: Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and Its Applications (JNLPBA '04), pp. 104-107.
16. Okazaki, N. (2007) CRFsuite: a fast implementation of conditional random fields (CRFs). http://www.chokkan.org/software/crfsuite.
17. Schwartz, A.S. and Hearst, M.A. (2003) A simple algorithm for identifying abbreviation definitions in biomedical text. Pac. Symp. Biocomput., 451-461.
18. Schuster, M. and Paliwal, K.K. (1997) Bidirectional recurrent neural networks. IEEE Trans. Signal Process., 45, 2673-2681.
19. Hochreiter, S. and Schmidhuber, J. (1997) Long short-term memory. Neural Comput., 9, 1735-1780.
20. Cho, K., van Merrienboer, B., Gulcehre, C. et al. (2014) Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1724-1734.
21. Mikolov, T., Sutskever, I., Chen, K. et al. (2013) Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems, pp. 3111-3119.
22. Hinton, G.E., Srivastava, N., Krizhevsky, A. et al. (2012) Improving neural networks by preventing co-adaptation of feature detectors. CoRR., abs/1207.0850.
23. Chang, C.-C. and Lin, C.-J. (2011) LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(3), article 27.