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State of the art models using deep neural networks have become very good in learning an accurate mapping from inputs to outputs.
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Comparing terms, concepts and semantic classes in WordNet and the Unified Medical Language System
Anita Burgun and Olivier Bodenreider. 2001 · 2001
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Two biomedical sublanguages: a description based on the theories of Zellig Harris
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Olivier Bodenreider. 2004 · 2004
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The pascal recognising textual entailment challenge
Ido Dagan, Oren Glickman, and Bernardo Magnini. 2006 · 2006
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LingPipe 4.1.0
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Extracting information from textual documents in the electronic health record: a review of recent research
Stephane M. Meystre, Guergana K. Savova, Karin C. Kipper-Schuler, and John F. Hurdle. 2008 · 2008
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Recognizing textual entailment: Rational, evaluation and approaches
Ido Dagan, Bill Dolan, Bernardo Magnini, and Dan Roth. 2009 · 2009
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An overview of metamap: historical perspective and recent advances
Alan R Aronson and François-Michel Lang. 2010 · 2010
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Ask not what textual entailment can do for you…
Mark Sammons, VG Vydiswaran, and Dan Roth. 2010 · 2010
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Formal representation of eligibility criteria: a literature review
Chunhua Weng, Samson W Tu, Ida Sim, and Rachel Richesson. 2010 · 2010
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Deep sparse rectifier neural networks
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Scikit-learn: Machine learning in Python
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Entailment-based text exploration with application to the health-care domain
Meni Adler, Jonathan Berant, and Ido Dagan. 2012 · 2012
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Interrater reliability: the kappa statistic
Mary L McHugh. 2012 · 2012
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013 · 2013
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On the properties of neural machine translation: Encoder-decoder approaches
Kyunghyun Cho, Bart Van Merriënboer, Dzmitry Bahdanau, and Yoshua Bengio. 2014 · 2014
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014 · 2014
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Semantic analysis and automatic corpus construction for entailment recognition in medical texts
Asma Ben Abacha, Duy Dinh, and Yassine Mrabet. 2015 · 2015
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015 · 2015
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A large annotated corpus for learning natural language inference
Addressing limited data for textual entailment across domains
Chaitanya Shivade, Preethi Raghavan, and Siddharth Patwardhan. 2016 · 2016
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Transfer learning for low-resource neural machine translation
Barret Zoph, Deniz Yuret, Jonathan May, and Kevin Knight. 2016 · 2016
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Fine-grained analysis of sentence embeddings using auxiliary prediction tasks
Yossi Adi, Einat Kermany, Yonatan Belinkov, Ofer Lavi, and Yoav Goldberg. 2017 · 2017
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Enriching word vectors with subword information
Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2017 · 2017
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Enhanced lstm for natural language inference
Qian Chen, Xiaodan Zhu, Zhen-Hua Ling, Si Wei, Hui Jiang, and Diana Inkpen. 2017 · 2017
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Supervised learning of universal sentence representations from natural language inference data
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Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015 · 2015
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Retrofitting word vectors to semantic lexicons
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Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba. 2015 · 2015
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Dbpedia–a large-scale, multilingual knowledge base extracted from wikipedia
Jens Lehmann, Robert Isele, Max Jakob, Anja Jentzsch, Dimitris Kontokostas, Pablo N Mendes, Sebastian Hellmann, Mohamed Morsey, Patrick Van Kleef, Sören Auer, et al. 2015 · 2015
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Textual inference for eligibility criteria resolution in clinical trials
Chaitanya Shivade, Courtney Hebert, Marcelo Lopetegui, Marie-Catherine De Marneffe, Eric Fosler-Lussier, and Albert M Lai. 2015 · 2015
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An overview of the bioasq large-scale biomedical semantic indexing and question answering competition
George Tsatsaronis, Georgios Balikas, Prodromos Malakasiotis, Ioannis Partalas, Matthias Zschunke, Michael R Alvers, Dirk Weissenborn, Anastasia Krithara, Sergios Petridis, Dimitris Polychronopoulos, et al. 2015 · 2015
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Oriol Vinyals, Łukasz Kaiser, Terry Koo, Slav Petrov, Ilya Sutskever, and Geoffrey Hinton. 2015 · 2015
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Alexis Conneau, Douwe Kiela, Holger Schwenk, Loic Barrault, and Antoine Bordes. 2017 · 2017
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Learned in translation: Contextualized word vectors
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The repeval 2017 shared task: Multi-genre natural language inference with sentence representations
Nikita Nangia, Adina Williams, Angeliki Lazaridou, and Samuel R Bowman. 2017 · 2017
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Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning
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Sunil Kumar Sahu and Ashish Anand. 2017 · 2017
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Recognizing mentions of adverse drug reaction in social media using knowledge-infused recurrent models
Gabriel Stanovsky, Daniel Gruhl, and Pablo Mendes. 2017 · 2017
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Annotation artifacts in natural language inference data
Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Samuel R Bowman, and Noah A Smith. 2018 · 2018
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Ji Young Lee, Franck Dernoncourt, and Peter Szolovits. 2018 · 2018
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Hypothesis only baselines in natural language inference
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Performance impact caused by hidden bias of training data for recognizing textual entailment
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A broad-coverage challenge corpus for sentence understanding through inference
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