Fetching the paper…
Reading the bibliography…
Clinical natural language processing requires methods that can address domain-specific challenges, such as complex medical terminology and clinical contexts.
Huggingface’s transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, et al. 2019 · 1910
Earlier work this paper cites.
Chemprot: a disease chemical biology database
Olivier Taboureau, Sonny Kim Nielsen, Karine Audouze, Nils Weinhold, Daniel Edsgärd, Francisco S Roque, Irene Kouskoumvekaki, Alina Bora, et al. 2010 · 2010
Earlier work this paper cites.
Ncbi disease corpus: A resource for disease name recognition and concept normalization
Rezarta Islamaj Dogan, Robert Leaman, and Zhiyong Lu. 2014 · 2014
Earlier work this paper cites.
A sense inventory for clinical abbreviations and acronyms created using clinical notes and medical dictionary resources
Sungrim Moon, Serguei Pakhomov, Nathan Liu, James O Ryan, and Genevieve B Melton. 2014 · 2014
Earlier work this paper cites.
Automatic semantic classification of scientific literature according to the hallmarks of cancer
Simon Baker, Ilona Silins, Yufan Guo, Imran Ali, Johan Högberg, Ulla Stenius, and Anna Korhonen. 2015 · 2015
Earlier work this paper cites.
Extraction of relations between genes and diseases from text and large-scale data analysis: implications for translational research
Àlex Bravo, Janet Piñero, Núria Queralt-Rosinach, Michael Rautschka, and Laura I Furlong. 2015 · 2015
Earlier work this paper cites.
The chemdner corpus of chemicals and drugs and its annotation principles
Martin Krallinger, Obdulia Rabal, Florian Leitner, Miguel Vazquez, David Salgado, Zhiyong Lu, Robert Leaman, Yanan Lu, Donghong Ji, Daniel M. Lowe, Roger A. Sayle, Riza Batista-Navarro, et al. 2015 · 2015
Earlier work this paper cites.
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
Earlier work this paper cites.
Recognizing question entailment for medical question answering
Asma Ben Abacha and Dina Demner-Fushman. 2016 · 2016
Earlier work this paper cites.
Biocreative V CDR task corpus: a resource for chemical disease relation extraction
Jiao Li, Yueping Sun, Robin J. Johnson, Daniela Sciaky, Chih-Hsuan Wei, Robert Leaman, Allan Peter Davis, Carolyn J. Mattingly, Thomas C. Wiegers, and Zhiyong Lu. 2016 · 2016
Earlier work this paper cites.
Assessing the state of the art in biomedical relation extraction: overview of the biocreative v chemical-disease relation (cdr) task
Chih-Hsuan Wei, Yifan Peng, Robert Leaman, Allan Peter Davis, Carolyn J Mattingly, Jiao Li, Thomas C Wiegers, and Zhiyong Lu. 2016 · 2016
Earlier work this paper cites.
Mednli — a natural language inference dataset for the clinical domain
Chaitanya Shivade. 2017 · 2017
Earlier work this paper cites.
Central moment discrepancy (CMD) for domain-invariant representation learning
Werner Zellinger, Thomas Grubinger, Edwin Lughofer, Thomas Natschläger, and Susanne Saminger-Platz. 2017 · 2017
Earlier work this paper cites.
Large language models are human-level prompt engineers
Yongchao Zhou, Andrei Ioan Muresanu, Ziwen Han, Keiran Paster, Silviu Pitis, Harris Chan, and Jimmy Ba. 2023 · 2017
Earlier work this paper cites.
Scitail: A textual entailment dataset from science question answering
Tushar Khot, Ashish Sabharwal, and Peter Clark. 2018 · 2018
Earlier work this paper cites.
Overview of the MEDIQA 2019 shared task on textual inference, question entailment and question answering
Asma Ben Abacha, Chaitanya Shivade, and Dina Demner-Fushman. 2019 · 2019
Earlier work this paper cites.
PubMedQA: A dataset for biomedical research question answering
Qiao Jin, Bhuwan Dhingra, Zhengping Liu, William Cohen, and Xinghua Lu. 2019 · 2019
Earlier work this paper cites.
Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter. 2019 · 2019
Earlier work this paper cites.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, et al. 2019 · 2019
Earlier work this paper cites.
Transfer learning in biomedical natural language processing: An evaluation of BERT and ELMo on ten benchmarking datasets
Yifan Peng, Shankai Yan, and Zhiyong Lu. 2019 · 2019
Earlier work this paper cites.
Sentence-BERT: Sentence embeddings using Siamese BERT-networks
Nils Reimers and Iryna Gurevych. 2019 · 2019
Earlier work this paper cites.
Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020 · 2020
Earlier work this paper cites.
MixText: Linguistically-informed interpolation of hidden space for semi-supervised text classification
Jiaao Chen, Zichao Yang, and Diyi Yang. 2020 · 2020
Earlier work this paper cites.
Generation and evaluation of artificial mental health records for natural language processing
Julia Ive, Natalia Viani, Joyce Kam, Lucia Yin, Somain Verma, Stephen Puntis, Rudolf N Cardinal, Angus Roberts, Robert Stewart, and Sumithra Velupillai. 2020 · 2020
Earlier work this paper cites.
Explainable automated fact-checking for public health claims
Neema Kotonya and Francesca Toni. 2020 · 2020
Cited alongside, same era.
Data augmentation using pre-trained transformer models
Varun Kumar, Ashutosh Choudhary, and Eunah Cho. 2020 · 2020
Cited alongside, same era.
Effective transfer learning for identifying similar questions: Matching user questions to COVID-19 faqs
Clara H. McCreery, Namit Katariya, Anitha Kannan, Manish Chablani, and Xavier Amatriain. 2020 · 2020
Cited alongside, same era.
Beyond accuracy: Behavioral testing of NLP models with CheckList
Marco Tulio Ribeiro, Tongshuang Wu, Carlos Guestrin, and Sameer Singh. 2020 · 2020
Cited alongside, same era.
Unsupervised data augmentation for consistency training
Qizhe Xie, Zihang Dai, Eduard H. Hovy, Thang Luong, and Quoc Le. 2020 · 2020
Cited alongside, same era.
SeqMix: Augmenting active sequence labeling via sequence mixup
ProGen: Progressive zero-shot dataset generation via in-context feedback
Jiacheng Ye, Jiahui Gao, Zhiyong Wu, Jiangtao Feng, Tao Yu, and Lingpeng Kong. 2022b · 2022
Later among the works it cites.
MELM: Data augmentation with masked entity language modeling for low-resource NER
Ran Zhou, Xin Li, Ruidan He, Lidong Bing, Erik Cambria, Luo Si, and Chunyan Miao. 2022 · 2022
Later among the works it cites.
Few-shot biomedical named entity recognition via knowledge-guided instance generation and prompt contrastive learning
Peng Chen, Jian Wang, Hongfei Lin, Di Zhao, and Zhihao Yang. 2023 · 2023
Closest in time.
Increasing diversity while maintaining accuracy: Text data generation with large language models and human interventions
John Chung, Ece Kamar, and Saleema Amershi. 2023 · 2023
Closest in time.
A survey on knowledge graphs for healthcare: Resources, application progress, and promise
Hejie Cui, Jiaying Lu, Shiyu Wang, Ran Xu, Wenjing Ma, Shaojun Yu, Yue Yu, Xuan Kan, Tianfan Fu, Chen Ling, Joyce Ho, Fei Wang, and Carl Yang. 2023 · 2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Rongzhi Zhang, Yue Yu, and Chao Zhang. 2020 · 2020
Cited alongside, same era.
Overview of the biocreative vii litcovid track: multi-label topic classification for covid-19 literature annotation
Qingyu Chen, Alexis Allot, Robert Leaman, Rezarta Islamaj Doğan, and Zhiyong Lu. 2021 · 2021
Cited alongside, same era.
Medically aware GPT-3 as a data generator for medical dialogue summarization
Bharath Chintagunta, Namit Katariya, Xavier Amatriain, and Anitha Kannan. 2021 · 2021
Cited alongside, same era.
Domain-specific language model pretraining for biomedical natural language processing
Yu Gu, Robert Tinn, Hao Cheng, Michael Lucas, Naoto Usuyama, Xiaodong Liu, Tristan Naumann, Jianfeng Gao, and Hoifung Poon. 2021 · 2021
Cited alongside, same era.
Umls-based data augmentation for natural language processing of clinical research literature
Tian Kang, Adler Perotte, Youlan Tang, Casey Ta, and Chunhua Weng. 2021 · 2021
Cited alongside, same era.
True few-shot learning with language models
Ethan Perez, Douwe Kiela, and Kyunghyun Cho. 2021 · 2021
Cited alongside, same era.
Evidence-based fact-checking of health-related claims
Mourad Sarrouti, Asma Ben Abacha, Yassine Mrabet, and Dina Demner-Fushman. 2021 · 2021
Cited alongside, same era.
Closest in time.
John Giorgi, Augustin Toma, Ronald Xie, Sondra Chen, Kevin R An, Grace X Zheng, and Bo Wang. 2023 · 2023
Closest in time.
Medalpaca–an open-source collection of medical conversational ai models and training data
Tianyu Han, Lisa C Adams, Jens-Michalis Papaioannou, Paul Grundmann, Tom Oberhauser, Alexander Löser, Daniel Truhn, and Keno K Bressem. 2023 · 2023
Closest in time.
The AI Revolution in Medicine: GPT-4 and Beyond
Peter Lee, Carey Goldberg, and Isaac Kohane. 2023 · 2023
Closest in time.
Synthetic data generation with large language models for text classification: Potential and limitations
Zhuoyan Li, Hangxiao Zhu, Zhuoran Lu, and Ming Yin. 2023 · 2023
Closest in time.
Utility of chatgpt in clinical practice
Jialin Liu, Changyu Wang, and Siru Liu. 2023 · 2023
Closest in time.
Tuning language models as training data generators for augmentation-enhanced few-shot learning
Yu Meng, Martin Michalski, Jiaxin Huang, Yu Zhang, Tarek Abdelzaher, and Jiawei Han. 2023 · 2023
Closest in time.
The imperative for regulatory oversight of large language models (or generative ai) in healthcare
Bertalan Meskó and Eric J Topol. 2023 · 2023
Closest in time.
Large language models encode clinical knowledge
Karan Singhal, Shekoofeh Azizi, Tao Tu, S Sara Mahdavi, Jason Wei, Hyung Won Chung, Nathan Scales, Ajay Tanwani, Heather Cole-Lewis, Stephen Pfohl, et al. 2023 · 2023
Closest in time.
Biomedical discovery through the integrative biomedical knowledge hub (ibkh)
Chang Su, Yu Hou, Manqi Zhou, Suraj Rajendran, Jacqueline RMA Maasch, Zehra Abedi, Haotan Zhang, Zilong Bai, Anthony Cuturrufo, Winston Guo, et al. 2023 · 2023
Closest in time.
Does synthetic data generation of llms help clinical text mining?
Ruixiang Tang, Xiaotian Han, Xiaoqian Jiang, and Xia Hu. 2023 · 2023
Closest in time.
Towards generalist biomedical ai
Tao Tu, Shekoofeh Azizi, Danny Driess, Mike Schaekermann, Mohamed Amin, Pi-Chuan Chang, Andrew Carroll, Chuck Lau, Ryutaro Tanno, Ira Ktena, et al. 2023 · 2023
Closest in time.
Let’s synthesize step by step: Iterative dataset synthesis with large language models by extrapolating errors from small models
Ruida Wang, Wangchunshu Zhou, and Mrinmaya Sachan. 2023 · 2023
Closest in time.
Michael Wornow, Yizhe Xu, Rahul Thapa, Birju Patel, Ethan Steinberg, Scott Fleming, Michael A Pfeffer, Jason Fries, and Nigam H Shah. 2023 · 2023
Closest in time.
Pmc-llama: Further finetuning llama on medical papers
Chaoyi Wu, Xiaoman Zhang, Ya Zhang, Yanfeng Wang, and Weidi Xie. 2023 · 2023
Closest in time.
Weakly-supervised scientific document classification via retrieval-augmented multi-stage training
Ran Xu, Yue Yu, Joyce Ho, and Carl Yang. 2023 · 2023
Closest in time.
Large language model as attributed training data generator: A tale of diversity and bias
Yue Yu, Yuchen Zhuang, Jieyu Zhang, Yu Meng, Alexander Ratner, Ranjay Krishna, Jiaming Shen, and Chao Zhang. 2023 · 2023
Closest in time.
Siren’s song in the ai ocean: A survey on hallucination in large language models
Yue Zhang, Yafu Li, Leyang Cui, Deng Cai, Lemao Liu, Tingchen Fu, Xinting Huang, Enbo Zhao, Yu Zhang, Yulong Chen, et al. 2023 · 2023
Closest in time.
Promptagent: Strategic planning with language models enables expert-level prompt optimization
Xinyuan Wang, Chenxi Li, Zhen Wang, Fan Bai, Haotian Luo, Jiayou Zhang, Nebojsa Jojic, Eric P Xing, and Zhiting Hu. 2024 · 2024
Closest in time.