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Zero-Shot and Few-Shot Classification of Biomedical Articles in Context of the COVID-19 Pandemic [article]

Simon Lupart, Benoit Favre, Vassilina Nikoulina, Salah Ait-Mokhtar
2022 arXiv   pre-print
In the context of the COVID-19 pandemic, MeSH descriptors have emerged in relation to articles published on the corresponding topic.  ...  Zero-shot classification is an adequate response for timely labeling of the stream of papers with MeSH categories.  ...  in zero-shot and few-shot results.  ... 
arXiv:2201.03017v2 fatcat:w7vdufiuyrfblli4triyk4mtgq

Zero-Shot and Few-Shot Classification of Biomedical Articles in Context of the COVID-19 Pandemic [article]

Simon Lupart, Benoit Favre, Vassilina Nikoulina, Salah Ait-Mokhtar
2022
In the context of the COVID-19 pandemic, MeSH descriptors have emerged in relation to articles published on the corresponding topic.  ...  Zero-shot classification is an adequate response for timely labeling of the stream of papers with MeSH categories.  ...  in zero-shot and few-shot results.  ... 
doi:10.48550/arxiv.2201.03017 fatcat:jxi7ne52h5gsrduazoxbp47qbm

A Self-supervised Approach for Semantic Indexing in the Context of COVID-19 Pandemic [article]

Nima Ebadi, Peyman Najafirad
2020 arXiv   pre-print
The pandemic has accelerated the pace at which COVID-19 scientific papers are published.  ...  We present a case study on a novel dataset that is based on COVID-19 papers published and manually indexed in PubMed.  ...  FUNDING The authors gratefully acknowledge the use of the services of Jetstream cloud, funded by National Science Foundation (NSF) awards 1445604, and the Cloud Technology Endowed Professorship.  ... 
arXiv:2010.03544v1 fatcat:u4gn3il65vgrzkkubwe46c37vi

A Search Engine for Discovery of Scientific Challenges and Directions [article]

Dan Lahav, Jon Saad Falcon, Bailey Kuehl, Sophie Johnson, Sravanthi Parasa, Noam Shomron, Duen Horng Chau, Diyi Yang, Eric Horvitz, Daniel S. Weld, Tom Hope
2022 arXiv   pre-print
We focus on a large corpus of interdisciplinary work relating to the COVID-19 pandemic, ranging from biomedicine to areas such as AI and economics.  ...  In experiments with 19 researchers and clinicians using our system, we outperform a popular scientific search engine in assisting knowledge discovery.  ...  Weld's work at the University of Washington is funded by ONR grant N00014-18-1-2193, NSF RAPID grant 2040196, the WR-F/Cable Professorship, and AI2.  ... 
arXiv:2108.13751v3 fatcat:j3jrkes2mfdbljmkicsyvsc7wm

Do We Need a Specific Corpus and Multiple High-Performance GPUs for Training the BERT Model? An Experiment on COVID-19 Dataset

Nontakan Nuntachit, Prompong Sugunnasil
2022 Machine Learning and Knowledge Extraction  
This article proposes a method of making an unsupervised model called a zero-shot classification model, based on the pre-trained BERT model.  ...  The COVID-19 pandemic has impacted daily lives around the globe. Since 2019, the amount of literature focusing on COVID-19 has risen exponentially.  ...  Acknowledgments: The authors wish to thank Associate Professor Juggapong Natwichai for his guidance and all supports. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/make4030030 fatcat:q4ci2lvldfgrthqf76seenssxe

A Few-Shot U-Net Deep Learning Model for COVID-19 Infected Area Segmentation in CT Images

Athanasios Voulodimos, Eftychios Protopapadakis, Iason Katsamenis, Anastasios Doulamis, Nikolaos Doulamis
2021 Sensors  
In this paper, we explore the efficacy of few-shot learning in U-Net architectures, allowing for a dynamic fine-tuning of the network weights as new few samples are being fed into the U-Net.  ...  In this paper, we scrutinize the effectiveness of deep learning models for semantic segmentation of pneumonia-infected area segmentation in CT images for the detection of COVID-19.  ...  The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.  ... 
doi:10.3390/s21062215 pmid:33810066 fatcat:j6pi7g33tbcbxiylgra5gsv2ja

HealthPrompt: A Zero-shot Learning Paradigm for Clinical Natural Language Processing [article]

Sonish Sivarajkumar, Yanshan Wang
2022 arXiv   pre-print
Zero-Shot Learning(ZSL) refers to the use of deep learning models to classify instances from new classes of which no training data have been seen before.  ...  Our experiments prove that prompts effectively capture the context of clinical texts and perform remarkably well without any training data.  ...  The funders had no role in the design of the study, collection, analysis, and interpretation of data and in preparation of the manuscript.  ... 
arXiv:2203.05061v1 fatcat:avus5stdqrcsji4fw5n7ghedim

Continual-T0: Progressively Instructing 50+ Tasks to Language Models Without Forgetting [article]

Thomas Scialom and Tuhin Chakrabarty and Smaranda Muresan
2022 arXiv   pre-print
In spite of the limited success of Continual Learning we show that Language Models can be continual learners.  ...  Language models trained on these instructions show strong zero-shot performance on several standard datasets.  ...  Covid QA (CQA) Instruction Output In the context of the COVID pandemic,who is at greater risk of dying from COVID19?  ... 
arXiv:2205.12393v1 fatcat:bafrscx74zh2xbfgg2hrej2qoe

COVIDRead: A Large-scale Question Answering Dataset on COVID-19 [article]

Tanik Saikh, Sovan Kumar Sahoo, Asif Ekbal, Pushpak Bhattacharyya
2021 arXiv   pre-print
To the best of our knowledge, we are the first to provide this kind of QA dataset in such a large volume on COVID-19.  ...  The dataset consists of Context-Answer-Question triples. Primarily the questions from the context are constructed in an automated way.  ...  But it can be helpful for evaluating the zero-shot or transfer capabilities of the existing models on COVID-19 domain.  ... 
arXiv:2110.09321v1 fatcat:ilhnlrof45estct74fbxtej5aa

BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models [article]

Nandan Thakur, Nils Reimers, Andreas Rücklé, Abhishek Srivastava, Iryna Gurevych
2021 arXiv   pre-print
Our results show BM25 is a robust baseline and re-ranking and late-interaction-based models on average achieve the best zero-shot performances, however, at high computational costs.  ...  We leverage a careful selection of 18 publicly available datasets from diverse text retrieval tasks and domains and evaluate 10 state-of-the-art retrieval systems including lexical, sparse, dense, late-interaction  ...  TREC-COVID [65] is an ad-hoc search challenge based on the CORD-19 dataset containing scientific articles related to the COVID-19 pandemic [69] .  ... 
arXiv:2104.08663v4 fatcat:fow5uqghbzggjclobtv7bpjtaa

Pre-trained Language Models in Biomedical Domain: A Systematic Survey [article]

Benyou Wang, Qianqian Xie, Jiahuan Pei, Prayag Tiwari, Zhao Li, Jie fu
2021 arXiv   pre-print
In this paper, we summarize the recent progress of pre-trained language models in the biomedical domain and their applications in biomedical downstream tasks.  ...  Particularly, we discuss the motivations and propose a taxonomy of existing biomedical PLMs. Their applications in biomedical downstream tasks are exhaustively discussed.  ...  Some extreme cases in machine learning are called 'zero-shot' or 'few-shot'.  ... 
arXiv:2110.05006v2 fatcat:aykwfhgi4jgmfovissgdvknny4

Deep Learning applications for COVID-19

Connor Shorten, Taghi M. Khoshgoftaar, Borko Furht
2021 Journal of Big Data  
AbstractThis survey explores how Deep Learning has battled the COVID-19 pandemic and provides directions for future research on COVID-19.  ...  We begin by evaluating the current state of Deep Learning and conclude with key limitations of Deep Learning for COVID-19 applications.  ...  Additionally, we acknowledge partial support by the NSF (IIS-2027890) . Opinions, findings, conclusions, or recommendations in this paper are the authors' and do not reflect the views of the NSF.  ... 
doi:10.1186/s40537-020-00392-9 pmid:33457181 pmcid:PMC7797891 fatcat:aokxo63z2rhdpfxo3egyf3xpcm

Multilingual Epidemic Event Extraction

Stephen Mutuvi, Emanuela Boros, Antoine Doucet, Gaël Lejeune, Adam Jatowt, Moses Odeo
2021 Zenodo  
The task of extracting epidemic events is defined as the detection of disease names and locations in a document.  ...  Our findings show the potential of pre-trained language models benefiting from the incorporation of unannotated data in the training process.  ...  results for the low-resourced languages in DAnIEL in the four few-shot scenarios (F1%).  ... 
doi:10.5281/zenodo.5779965 fatcat:jz2vlx77irerbncqt3ncmkjjm4

Rapidly Bootstrapping a Question Answering Dataset for COVID-19 [article]

Raphael Tang, Rodrigo Nogueira, Edwin Zhang, Nikhil Gupta, Phuong Cam, Kyunghyun Cho, Jimmy Lin
2020 arXiv   pre-print
evaluating the zero-shot or transfer capabilities of existing models on topics specifically related to COVID-19.  ...  We present CovidQA, the beginnings of a question answering dataset specifically designed for COVID-19, built by hand from knowledge gathered from Kaggle's COVID-19 Open Research Dataset Challenge.  ...  Acknowledgments This work would not have been possible without the efforts of all the data scientists and curators who have participated in Kaggle's COVID-19 Open Research Dataset Challenge.  ... 
arXiv:2004.11339v1 fatcat:7aevsmdxqbdl3gxvcoqpnf2zta

Deep learning for drug repurposing: methods, databases, and applications [article]

Xiaoqin Pan, Xuan Lin, Dongsheng Cao, Xiangxiang Zeng, Philip S. Yu, Lifang He, Ruth Nussinov, Feixiong Cheng
2022 arXiv   pre-print
Finally, we present applications of drug repurposing to fight the COVID-19 pandemic, and outline its future challenges.  ...  However, comprehensively obtaining and productively integrating available knowledge and big biomedical data to effectively advance deep learning models is still challenging for drug repurposing in other  ...  to fight the crisis of COVID-19 pandemic(Figure 5 ).  ... 
arXiv:2202.05145v1 fatcat:5oqujy2daffdpa33b4cbrg6hqy
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