Convolutional neural networks for biomedical text classification: Application in indexing biomedical articles

BCB 2015 - 6th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics

Volumn , Issue , 2015, Pages 258-267

  Rios, Anthony ^a   Kavuluru, Ramakanth ^b  

^a UNIVERSITY OF KENTUCKY   (United States)

^b UNIVERSITY OF KENTUCKY   (United States)

Author keywords

Convolutional neural networks; Medical subject headings; Text classification

Indexed keywords

BINS; BIOINFORMATICS; BIOMEDICAL ENGINEERING; CLASSIFICATION (OF INFORMATION); CONVOLUTION; INDEXING (OF INFORMATION); INFORMATION SCIENCE; MESH GENERATION; MESH NETWORKING; NATURAL LANGUAGE PROCESSING SYSTEMS; NEURAL NETWORKS; SEMANTICS; VECTOR SPACES;

ADDITIONAL EXPERIMENTS; CONVOLUTIONAL NEURAL NETWORK; DISCRIMINATIVE APPROACH; MEDICAL SUBJECT HEADINGS; NATIONAL LIBRARY OF MEDICINES; SCIENTIFIC LITERATURE; TEXT CLASSIFICATION; TRADITIONAL APPROACHES;

TEXT PROCESSING;

EID: 84963568576     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2808719.2808746     Document Type: Conference Paper

References (40)

1. A. Aronson, J. Mork, C. Gay, S. Humphrey, and W. Rogers. The NLM indexing initiative's medical text indexer. In Proceedings of MEDINFO, 2004.
2. A. R. Aronson and F.-M. Lang. An overview of metamap: historical perspective and recent advances. Journal of the American Medical Informatics Association, 17(3):229-236, 2010.
3. Y. Bengio, R. Ducharme, P. Vincent, and C. Janvin. A neural probabilistic language model. The Journal of Machine Learning Research, 3:1137-1155, 2003.
4. J. Bergstra, O. Breuleux, F. Bastien, P. Lamblin, R. Pascanu, G. Desjardins, J. Turian, D. Warde-Farley, and Y. Bengio. Theano: a CPU and GPU math expression compiler. In Proceedings of the Python for Scientific Computing Conference (SciPy), June 2010. Oral Presentation.
5. D. Blei and J. Lafferty. Correlated topic models. Advances in neural information processing systems, 18:147, 2006.
6. D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. The Journal of machine Learning research, 3:993-1022, 2003.
7. D. D. A. Bui and Q. Zeng-Treitler. Learning regular expressions for clinical text classification. Journal of the American Medical Informatics Association, pages amiajnl-2013, 2014.
8. D. Cameron, R. Kavuluru, T. C. Rindesch, A. P. Sheth, K. Thirunarayan, and O. Bodenreider. Context-driven automatic subgraph creation for literature-based discovery. Journal of biomedical informatics, 54:141-157, 2015.
9. R. Collobert and J. Weston. A unified architecture for natural language processing: Deep neural networks with multitask learning. In Proceedings of the 25th international conference on Machine learning, pages 160-167. ACM, 2008.
10. R. Collobert, J. Weston, L. Bottou, M. Karlen, K. Kavukcuoglu, and P. Kuksa. Natural language processing (almost) from scratch. Journal of Machine Learning Research, 12:2493-2537, 2011.
11. J. Deng, N. Ding, Y. Jia, A. Frome, K. Murphy, S. Bengio, Y. Li, H. Neven, and H. Adam. Large-scale object classification using label relation graphs. In Computer Vision-ECCV 2014, pages 48-64. Springer, 2014.
12. X. Glorot, A. Bordes, and Y. Bengio. Deep sparse rectifier networks. In Proceedings of the 14th International Conference on Artificial Intelligence and Statistics. JMLR W&CP Volume, volume 15, pages 315-323, 2011.
13. M. Huang, A. Névéol, and Z. Lu. Recommending mesh terms for annotating biomedical articles. Journal of the American Medical Informatics Association, 18(5):660-667, 2011.
14. A. Jimeno-Yepes, J. G. Mork, D. Demner-Fushman, and A. R. Aronson. A one-size-fits-all indexing method does not exist: Automatic selection based on meta-learning. Journal of Computing Science and Engineering, 6(2):151-160, 2012.
15. A. Jimeno Yepes, L. Plaza, J. Carrillo-de Albornoz, J. G. Mork, and A. R. Aronson. Feature engineering for medline citation categorization with mesh. BMC Bioinformatics, 16(1):113, 2015.
16. T. Joachims. A support vector method for multivariate performance measures. In Proceedings of the 22nd international conference on Machine learning, pages 377-384. ACM, 2005.
17. N. Kalchbrenner, E. Grefenstette, and P. Blunsom. A convolutional neural network for modeling sentences. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 655-665, Baltimore, Maryland, June 2014. Association for Computational Linguistics.
18. R. Kavuluru and Y. Lu. Leveraging output term co-occurrence frequencies and latent associations in predicting medical subject headings. Data & Knowledge Engineering, 94(Part B):189-201, 2014.
19. Y. Kim. Convolutional neural networks for sentence classification. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1746-1751, Doha, Qatar, October 2014. Association for Computational Linguistics.
20. S. Kiritchenko, X. Zhu, and S. M. Mohammad. Sentiment analysis of short informal texts. Journal of Artificial Intelligence Research, pages 723-762, 2014.
21. K. Liu, J. Wu, S. Peng, C. Zhai, and S. Zhu. The fudan-uiuc participation in the BioASQ challenge task 2a: The antinomyra system. Proceedings of Question Answering Lab at the Conference and Labs of the Evaluation Forum (CLEF), 2014.
22. T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean. Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems, pages 3111-3119, 2013.
23. V. Nair and G. E. Hinton. Rectified linear units improve restricted Boltzmann machines. In Proceedings of the 27th International Conference on Machine Learning (ICML-10), pages 807-814, 2010.
24. J. Nam, J. Kim, E. L. Mencía, I. Gurevych, and J. Fürnkranz. Large-scale multi-label text classificationâ. Trevisiting neural networks. In Machine Learning and Knowledge Discovery in Databases, pages 437-452. Springer, 2014.
25. J. Ngiam, A. Coates, A. Lahiri, B. Prochnow, Q. V. Le, and A. Y. Ng. On optimization methods for deep learning. In Proceedings of the 28th International Conference on Machine Learning (ICML-11), pages 265-272, 2011.
26. J. Read, B. Pfahringer, G. Holmes, and E. Frank. Classifier chains for multi-label classification. Machine Learning, 85(3):335-359, 2011.
27. R. Socher. Recursive Deep Learning for Natural Language Processing and Computer Vision. PhD thesis, Department of Computer Science, Stanford University, 2014.
28. R. Socher, A. Perelygin, J. Y. Wu, J. Chuang, C. D. Manning, A. Y. Ng, and C. Potts. Recursive deep models for semantic compositionality over a sentiment treebank. In Proceedings of the conference on empirical methods in natural language processing (EMNLP), volume 1631, page 1642. Citeseer, 2013.
29. S. Sohn, W. Kim, D. C. Comeau, and W. J. Wilbur. Optimal training sets for bayesian prediction of MeSH assignment. Journal of the American Medical Informatics Association, 15(4):546-553, 2008.
30. N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov. Dropout: A simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research, 15(1):1929-1958, 2014.
31. M. Steyvers, P. Smyth, M. Rosen-Zvi, and T. Griffiths. Probabilistic author-topic models for information discovery. In Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 306-315. ACM, 2004.
32. G. Tsatsaronis, G. Balikas, P. Malakasiotis, I. Partalas, M. Zschunke, M. R. Alvers, D. Weissenborn, A. Krithara, S. Petridis, D. Polychronopoulos, et al. An overview of the bioasq large-scale biomedical semantic indexing and question answering competition. BMC bioinformatics, 16(1):138, 2015.
33. A. J. Yepes and R. Berlanga. Knowledge based word-concept model estimation and refinement for biomedical text mining. Journal of biomedical informatics, 53:300-307, 2015.
34. A. J. Yepes, A. MacKinlay, J. Bedo, R. Garnavi, and Q. Chen. Deep belief networks and biomedical text categorisation. In Proceedings of the Twelfth Annual Workshop of the Australasia Language Technology Association, page 123, 2014.
35. A. J. Yepes, J. G. Mork, D. Demner-Fushman, and A. R. Aronson. Comparison and combination of several MeSH indexing approaches. In AMIA Annual Symposium Proceedings, volume 2013, page 709. American Medical Informatics Association, 2013.
36. M. Yetisgen-Yildiz and W. Pratt. The effect of feature representation on medline document classification. In Proceedings of AMIA Symposium, volume 2005, pages 849-853. American Medical Informatics Association, 2005.
37. M. D. Zeiler. ADADELTA: an adaptive learning rate method. arXiv preprint arXiv:1212.5701, 2012.
38. M. D. Zeiler, D. Krishnan, G. W. Taylor, and R. Fergus. Deconvolutional networks. In Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, pages 2528-2535. IEEE, 2010.
39. M.-L. Zhang and Z.-H. Zhou. Multilabel neural networks with applications to functional genomics and text categorization. Knowledge and Data Engineering, IEEE Transactions on, 18(10):1338-1351, 2006.
40. Z.-H. Zhou. Ensemble methods: foundations and algorithms. CRC Press, 2012.