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Recently, biomedical version of embeddings obtained from language models such as BioELMo have shown state-of-the-art results for the textual inference task in the medical domain.
Biobert: a pre-trained biomedical language representation model for biomedical text mining
Jinhyuk Lee, Wonjin Yoon, Sungdong Kim, Donghyeon Kim, Sunkyu Kim, Chan Ho So, and Jaewoo Kang. 2019 · 1901
Earlier work this paper cites.
Probing biomedical embeddings from language models
Qiao Jin, Bhuwan Dhingra, William W. Cohen, and Xinghua Lu. 2019 · 1904
Earlier work this paper cites.
The unified medical language system (umls): integrating biomedical terminology
Olivier Bodenreider. 2004 · 2004
Earlier work this paper cites.
An overview of metamap: Historical perspective and recent advances
A. R. Aronson and F.-M. Lang. 2010 · 2010
Earlier work this paper cites.
Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014 · 2014
Earlier work this paper cites.
Embedding entities and relations for learning and inference in knowledge bases
Xiaodong He Jianfeng Gao Bishan Yang, Wen-tau Yih and Li Deng. 2015 · 2015
Earlier work this paper cites.
A large annotated corpus for learning natural language inference
Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015 · 2015
Earlier work this paper cites.
Mimic-iii, a freely accessible critical care database
Lu Shen H Lehman Li-wei Mengling Feng Mohammad Ghassemi Benjamin Moody Peter Szolovits Leo Anthony Celi Alistair EW Johnson, Tom J Pollard and Roger G Mark. 2016 · 2016
Cited alongside, same era.
A fast unified model for parsing and sentence understanding
Samuel R Bowman, Raghav Gupta, Jon Gauthier, Christopher D Manning, Abhinav Rastogi, and Christopher Potts. 2016 · 2016
Cited alongside, same era.
Reading and thinking: Re-read lstm unit for textual entailment recognition
Lei Sha, Baobao Chang, Zhifang Sui, and Sujian Li. 2016 · 2016
Cited alongside, same era.
Enriching word vectors with subword information
Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2017 · 2017
Cited alongside, same era.
Enhanced lstm for natural language inference
Qian Chen, Xiaodan Zhu, Zhen-Hua Ling, Si Wei, Hui Jiang, and Diana Inkpen. 2017 · 2017
Cited alongside, same era.
Knowledge base completion: Baselines strike back
Neural natural language inference models enhanced with external knowledge
Qian Chen, Xiaodan Zhu, Zhen-Hua Ling, Diana Inkpen, and Si Wei. 2018 · 2018
Later among the works it cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
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Deep contextualized word representations
Matthew Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
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Lessons from natural language inference in the clinical domain
Alexey Romanov and Chaitanya Shivade. 2018 · 2018
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Compare, compress and propagate: Enhancing neural architectures with alignment factorization for natural language inference
Yi Tay, Luu Anh Tuan, and Siu Cheung Hui. 2018 · 2018
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Rudolf Kadlec, Ondrej Bajgar, and Jan Kleindienst. 2017 · 2017
Cited alongside, same era.
Neural tree indexers for text understanding
Tsendsuren Munkhdalai and Hong Yu. 2017 · 2017
Cited alongside, same era.
Adina Williams, Nikita Nangia, and Samuel Bowman. 2018 · 2018
Later among the works it cites.
Incorporating domain knowledge into natural language inference on clinical texts
Mingming Lu, Yu Fang, Fengqi Yan, and Maozhen Li. 2019 · 2019
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