Citation: | ZHANG Yajun, LIU Zongtian, ZHOU Wen, “Biomedical Named Entity Recognition Based on Self-supervised Deep Belief Network,” Chinese Journal of Electronics, vol. 29, no. 3, pp. 455-462, 2020, doi: 10.1049/cje.2020.03.001 |
T.h. Tsai, W.C. Chou, S.H. Wu, et al., “Integrating linguistic knowledge into a conditional random field framework to identify biomedical named entities”, Expert Systems with Applications, Vol.30, No.1, pp.117-128, 2006.
|
K.J. Lee, Y.S. Hwang and H.C. Rim, “Two-phase biomedical named entity recognition based on svms”, Proc. of the ACL 2003 Workshop on Natural Language Processing in Biomedicine, Sapporo, Hokkaido, Japan, pp.33-40, 2003.
|
S.F. Altschul, T.L. Madden, A.A. Schaffer, et al., “Gapped blast and psi-blast: A new generation of protein database search programs”, Nucleic Acids Research, Vol.25, No.17, pp.3389-3402, 1997.
|
K.J. Lee, Y.S. Hwang, S. Kim, et al., “Named entity recognition using two-phase model based on svms”, Journal of Biomedical Informatics, Vol.37, No.6, pp.436-447, 2004.
|
Y. Song, E. Kim, G.G. Lee, et al., “Posbiotmner: A trainable biomedical named-entity recognition system”, Bioinformatics, Vol.21, No.11, pp.2794-2796, 2005.
|
R.T.H. Tsai, C.L. Sung, H.J. Dai, et al., “Nerbio: Using selected word conjunctions,term normalization, and global patterns to improve biomedical named entity recognition”, BMC Bioinformatics, Vol.7, No.5, pp.5-11, 2006.
|
Y. Tsuruoka and J. Tsujii, “Boosting precision and recall of dictionary-based protein name recognition”, Proc. of the ACL 2003 Workshop on Natural Language Processing in Biomedicine, Sapporo, Hokkaido, Japan, pp.41-48, 2003.
|
Z. Yang, H. Lin and Y. Li, “Exploiting the performance of dictionary-based bio-entity name recognition in biomedical literature”, Computational Biology and Chemistry, Vol.32, No.4, pp.287-291, 2008.
|
M. J. Schuemie, B. Mons, M. Weeber, et al., “Evaluation of techniques for increasing recall in a dictionary approach to gene and protein name identification”, Journal of Biomedical Informatics, Vol.40, No.3, pp.316-324, 2007.
|
K. Franzen, G. Eriksson, F. Olsson, et al., “Protein names and how to find them”, International Journal of Medical Informatics, Vol.67, No.1, pp.49-61, 2002.
|
D. Hanisch, K. Fundel, H.T. Mevissen, et al., “Rule-based protein and gene entity recognition”, BMC Bioinformatics, Vol.6, No.1, pp.1-14, 2005.
|
Y. Tsuruoka, J. McNaught and S. Ananiadou, “Normalizing biomedical terms by minimizing ambiguity and variability”, BMC Bioinformatics, Vol.9, No.3, pp.2-3, 2008.
|
G.E. Hinton and R.R. Salakhutdinov, “Reducing the dimensionality of data with neural networks”, Science, Vol.313, No.5786, pp.504-507, 2006.
|
G.E. Hinton, “Training products of experts by minimizing contrastive divergence”, Neural Computation, Vol.14, No.8, pp.1771-1880, 2002.
|
R. Salakhutdinov and G. Hinton, “Deep Boltzmann machines”, Journal of Machine Learning Research, Vol.5, No.2, pp.1967-2006, 2009.
|
G.E. Hinton, S. Osindero and Y.W. Teh, “A fast learning algorithm for deep belief nets”, Neural Computation, Vol.18, No.7, pp.1527-1554, 2006.
|
C. Guoan, “Fast backpropagation learning using optimal learning rate and momentum”, Journal of Southeast University, Vol.10, No.3, pp.517-527, 1999.
|
S. Zhao, “Named entity recognition in biomedical texts using an hmm model”, Proc. of the International Joint Workshop on Natural Language Processing in Biomedicine and Its Applications, pp.84-87, 2004.
|
B. Settles, “Biomedical named entity recognition using conditional random fields and rich feature sets”, Proc. of the International Joint Workshop on Natural Language Processing in Biomedicine and Its Applications, pp.104-107, 2004.
|
L. Yao, H. Liu, Y. Liu, et al., “Biomedical named entity recognition based on deep neutral network”, International Journal of Hybrid InformationTechnology, Vol.8, No.8, pp.279-288, 2015.
|