Recurrent neural network models for disease name recognition using domain invariant features

54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - Long Papers

Volumn 4, Issue , 2016, Pages 2216-2225

  Sahu, Sunil Kumar ^a   Anand, Ashish ^a  

^a INDIAN INSTITUTE OF TECHNOLOGY GUWAHATI   (India)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); COMPUTATIONAL LINGUISTICS;

CLASSIFICATION TASKS; CONVOLUTION NEURAL NETWORK; FEATURE ENGINEERINGS; INVARIANT FEATURES; RECURRENT NEURAL NETWORK (RNN); RECURRENT NEURAL NETWORK MODEL; STATE OF THE ART; STATE-OF-THE-ART PERFORMANCE;

RECURRENT NEURAL NETWORKS;

EID: 85011961552     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.18653/v1/p16-1209     Document Type: Conference Paper

References (28)

1. Frédéric Bastien, Pascal Lamblin, Razvan Pascanu, James Bergstra, Ian J. Goodfellow, Arnaud Bergeron, Nicolas Bouchard, and Yoshua Bengio. 2012. Theano: new features and speed improvements.
2. Yoshua Bengio, Réjean Ducharme, Pascal Vincent, and Christian Janvin. 2003. A neural probabilistic language model. J. Mach. Learn. Res., 3: 1137-1155, March.
3. Yoshua Bengio, Nicolas Boulanger-Lewandowski, and Razvan Pascanu. 2013. Advances in optimizing recurrent networks. In Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on, pages 8624-8628. IEEE.
4. KyungHyun Cho, Bart van Merrienboer, Dzmitry Bahdanau, and Yoshua Bengio. 2014. On the properties of neural machine translation: Encoder-decoder approaches. CoRR, abs/1409.1259.
5. Junyoung Chung, Çaglar Gülçehre, KyungHyun Cho, and Yoshua Bengio. 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. CoRR, abs/1412.3555.
6. Ronan Collobert and Jason Weston. 2008. 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.
7. Ronan Collobert, Jason Weston, Léon Bottou, Michael Karlen, Koray Kavukcuoglu, and Pavel Kuksa. 2011. Natural language processing (almost) from scratch. J. Mack Learn. Res., 12: 2493-2537, November.
8. Rezarta Islamaj Dogan, Robert Leaman, and Zhiyong Lu. 2014. NCBI disease corpus: A resource for disease name recognition and concept normalization. Journal of Biomedical Informatics, 47: 1-10.
9. Cicero Nogueira dos Santos and Bianca Zadrozny. 2014. Learning character-level representations for part-of-speech tagging. In International Conference on Machine Learning (ICML), Volume 32, pages 1818-1826. JMLR W&CP.
10. Cicero dos Santos, Victor Guimaraes, RJ Niterói, and Rio de Janeiro. 2015. Boosting named entity recognition with neural character embeddings. Proceedings of NEWS 2015 The Fifth Named Entities Workshop, page 9.
11. Rezarta Islamaj Dogan and Zhiyong Lu. 2012. An improved corpus of disease mentions in pubmed citations. In Proceedings of the 2012 Workshop on Biomedical Natural Language Processing, BioNLP '12, pages 91-99, Stroudsburg, PA, USA. Association for Computational Linguistics.
12. John Duchi, Elad Hazan, and Yoram Singer. 2010. Adaptive subgradient methods for online learning and stochastic optimization. Technical Report UCB/EECS-2010-24, EECS Department, University of California, Berkeley, Mar.
13. Alex Graves, Marcus Liwicki, Santiago Fernández, Roman Bertolami, Horst Bunke, and Jürgen Schmidhuber. 2009. A novel connectionist system for unconstrained handwriting recognition. IEEE Trans. Pattern Anal. Mach. Intell., 31(5): 855-868, May.
14. Alex Graves. 2013. Generating sequences with recurrent neural networks. arXiv preprint arXiv: 1308.0850.
15. Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural Comput., 9(8): 1735-1780, November.
16. Zhiheng Huang, Wei Xu, and Kai Yu 2015. Bidirectional LSTM-CRF models for sequence tagging. CoRR, abs/1508.01991.
17. Matthieu Labeau, Kevin Lser, and Alexandre Allauzen. 2015. Non-lexical neural architecture for fine-grained pos tagging. In Llus Mrquez, Chris Callison-Burch, Jian Su, Daniele Pighin, and Yuval Marton, editors, EMNLP, pages 232-237. The Association for Computational Linguistics.
18. Robert Leaman and Graciela Gonzalez. 2008. Banner: An executable survey of advances in biomedical named entity recognition. In Russ B. Altman, A. Keith Dunker, Lawrence Hunter, Tiffany Murray, and Teri E. Klein, editors, Pacific Symposium on Biocomputing, pages 652-663. World Scientific.
19. Robert Leaman, Christopher Miller, and G Gonzalez. 2009. Enabling recognition of diseases in biomedical text with machine learning: corpus and benchmark. Proceedings of the 2009 Symposium on Languages in Biology and Medicine, 82(9).
20. Gang Li 2012. Disease mention recognition using soft-margin svm. Training, 593: 5-148.
21. Md. Faisal Mahbub Chowdhury and Alberto Lavelli. 2010. Disease mention recognition with specific features. In Proceedings of the 2010 Workshop on Biomedical Natural Language Processing, BioNLP '10, pages 83-90, Stroudsburg, PA, USA. Association for Computational Linguistics.
22. Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013a. Efficient estimation of word representations in vector space. arXiv preprint arXiv: 1301.3781.
23. Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013b. Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems, pages 3111-3119.
24. Azadeh Nikfarjam, Abeed Sarker, Karen O'Connor, Rachel Ginn, and Graciela Gonzalez. 2015. Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features. Journal of the American Medical Informatics Association, page ocu041.
25. Razvan Pascanu, Tomas Mikolov, and Yoshua Bengio. 2012. Understanding the exploding gradient problem. CoRR, abs/1211.5063.
26. Barbara Rosario and Marti A. Hearst. 2004. Classifying semantic relations in bioscience texts. In Proceedings of the 42Nd Annual Meeting on Association for Computational Linguistics, ACL '04, Stroudsburg, PA, USA. Association for Computational Linguistics.
27. B. Settles. 2005. ABNER: An open source tool for automatically tagging genes, proteins, and other entity names in text. Bioinformatics, 21(14): 3191-3192.
28. MUNEEB TH, Sunil Sahu, and Ashish Anand. 2015. Evaluating distributed word representations for capturing semantics of biomedical concepts. In Proceedings of BioNLP 15, pages 158-163, Beijing, China, July. Association for Computational Linguistics.