S2ORC: The Semantic Scholar Open Research Corpus

Lo, Kyle; Wang, Lucy Lu; Neumann, Mark E; Kinney, Rodney; Weld, Daniel S.

doi:10.18653/v1/2020.acl-main.447

Cited by 274 publications

(234 citation statements)

References 44 publications

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“…We observed documentation of this explosive growth as well when surveying Literature Mining systems built on top of this dataset. The CORD-19 data is cleaned and implemented with the same system used for the Semantic Scholar Open Research Corpus [41].…”

Section: Literature Miningmentioning

confidence: 99%

Deep Learning applications for COVID-19

2021

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This survey explores how Deep Learning has battled the COVID-19 pandemic and provides directions for future research on COVID-19. We cover Deep Learning applications in Natural Language Processing, Computer Vision, Life Sciences, and Epidemiology. We describe how each of these applications vary with the availability of big data and how learning tasks are constructed. We begin by evaluating the current state of Deep Learning and conclude with key limitations of Deep Learning for COVID-19 applications. These limitations include Interpretability, Generalization Metrics, Learning from Limited Labeled Data, and Data Privacy. Natural Language Processing applications include mining COVID-19 research for Information Retrieval and Question Answering, as well as Misinformation Detection, and Public Sentiment Analysis. Computer Vision applications cover Medical Image Analysis, Ambient Intelligence, and Vision-based Robotics. Within Life Sciences, our survey looks at how Deep Learning can be applied to Precision Diagnostics, Protein Structure Prediction, and Drug Repurposing. Deep Learning has additionally been utilized in Spread Forecasting for Epidemiology. Our literature review has found many examples of Deep Learning systems to fight COVID-19. We hope that this survey will help accelerate the use of Deep Learning for COVID-19 research.

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Section: Literature Miningmentioning

confidence: 99%

Deep Learning applications for COVID-19

2021

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“…The corpus combines papers from the PubMed Central (PMC), PubMed, World Health Organization (WHO)’s COVID-19 database ( https://www.who.int/emergencies/diseases/novel-coronavirus-2019/global-research-on-novel-coronavirus-2019-ncov ) and preprint servers bioRxiv, medRxiv and arXiv. Paper metadata from these sources are harmonized, PDFs are converted into machine-readable JSON using the S2ORC pipeline described in [ 54 ] and HTML representations of tables in papers are added using IBM Watson Discovery’s Global Table Extractor [ 115 ]. As of 15 September 2020, the corpus contains more than 260 000 paper entries (with 105 000 full text entries).…”

Section: Text Mining Corporamentioning

confidence: 99%

Text mining approaches for dealing with the rapidly expanding literature on COVID-19

Wang

2020

Briefings in Bioinformatics

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More than 50 000 papers have been published about COVID-19 since the beginning of 2020 and several hundred new papers continue to be published every day. This incredible rate of scientific productivity leads to information overload, making it difficult for researchers, clinicians and public health officials to keep up with the latest findings. Automated text mining techniques for searching, reading and summarizing papers are helpful for addressing information overload. In this review, we describe the many resources that have been introduced to support text mining applications over the COVID-19 literature; specifically, we discuss the corpora, modeling resources, systems and shared tasks that have been introduced for COVID-19. We compile a list of 39 systems that provide functionality such as search, discovery, visualization and summarization over the COVID-19 literature. For each system, we provide a qualitative description and assessment of the system’s performance, unique data or user interface features and modeling decisions. Many systems focus on search and discovery, though several systems provide novel features, such as the ability to summarize findings over multiple documents or linking between scientific articles and clinical trials. We also describe the public corpora, models and shared tasks that have been introduced to help reduce repeated effort among community members; some of these resources (especially shared tasks) can provide a basis for comparing the performance of different systems. Finally, we summarize promising results and open challenges for text mining the COVID-19 literature.

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“…We extract figures and captions from open access papers in PubMed Central using the results of Siegel et al (2018). To add inline references, we match extracted figures to corresponding figures in the publicly-available S2ORC corpus (Lo et al, 2020), then extract inline references for these figures from the S2ORC full text (see Appendix A for details). We exclude figures found in ROCO (Pelka et al, 2018) to create a disjoint dataset, though we identify and release inline references for ROCO figures.…”

Section: The Medicat Datasetmentioning

confidence: 99%

MedICaT: A Dataset of Medical Images, Captions, and Textual References

Subramanian

Wang²,

Bogin

et al. 2020

Findings of the Association for Computational Linguistics: EMNLP 2020

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Understanding the relationship between figures and text is key to scientific document understanding. Medical figures in particular are quite complex, often consisting of several subfigures (75% of figures in our dataset), with detailed text describing their content. Previous work studying figures in scientific papers focused on classifying figure content rather than understanding how images relate to the text. To address challenges in figure retrieval and figure-to-text alignment, we introduce MED-ICAT, a dataset of medical images in context. MEDICAT consists of 217K images from 131K open access biomedical papers, and includes captions, inline references for 74% of figures, and manually annotated subfigures and subcaptions for a subset of figures. Using MEDICAT, we introduce the task of subfigure to subcaption alignment in compound figures and demonstrate the utility of inline references in image-text matching. Our data and code can be accessed at https://github.com/allenai/medicat.

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S2ORC: The Semantic Scholar Open Research Corpus

Cited by 274 publications

References 44 publications

Deep Learning applications for COVID-19

Deep Learning applications for COVID-19

Text mining approaches for dealing with the rapidly expanding literature on COVID-19

MedICaT: A Dataset of Medical Images, Captions, and Textual References

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