CORD19STS: COVID-19 Semantic Textual Similarity Dataset

Guo, Xiao; Mirzaalian, Hengameh; Sabir, Ekraam; Jaiswal, Ayush; AbdAlmageed, Wael

doi:10.48550/arxiv.2007.02461

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…Many machine learning contributions have relied on CORD-19, and derived datasets have been created. Examples include entity recognition and concept extraction [133,82,24,103,131], question answering [119], textual evidence mining [132], text similarity [60,135], search engines [143,89,144,47], graphical interfaces to explore scientific evidence on COVID-19 [127,68], keyword extraction and clustering [41], summarization [79]. Text mining approaches to COVID-19 literature have been critically assessed in a recent review [129].…”

Section: Machine Learning Applicationsmentioning

confidence: 99%

Meta-research on COVID-19: An overview of the early trends

Colavizza

2021

Preprint

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COVID-19 is having a dramatic impact on research and researchers. The pandemic has underlined the severity of known challenges in research and surfaced new ones, but also accelerated the adoption of innovations and manifested new opportunities. This review considers early trends emerging from meta-research on COVID-19. In particular, it focuses on the following topics: i) mapping COVID-19 research; ii) data and machine learning; iii) research practices including open access and open data, reviewing, publishing and funding; iv) communicating research to the public; v) the impact of COVID-19 on researchers, in particular with respect to gender and career trajectories. This overview finds that most early meta-research on COVID-19 has been reactive and focused on short-term questions, while more recently a shift to consider the long-term consequences of COVID-19 is taking place. Based on these findings, the author speculates that some aspects of doing research during COVID-19 are more likely to persist than others. These include: the shift to virtual for academic events such as conferences; the use of openly accessible pre-prints; the 'datafication' of scholarly literature and consequent broader adoption of machine learning in science communication; the public visibility of research and researchers on social and online media.

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Section: Machine Learning Applicationsmentioning

confidence: 99%

Meta-research on COVID-19: An overview of the early trends

Colavizza

2021

Preprint

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“…Measuring the similarity between two sentences (or short texts) consists in evaluating to what extent the meaning of these sentences is close [2]. This task semantic textual similarity (STS) is often used in several important areas of the automatic language processing (TAL), among which we can mention the search for information, the categorization of texts [3], the summary of text [4], and the machine translation [5]. When we talk about similarity we talk about classification, clustering (or clustering) to describe data partitioning and a cluster is then a set of data or elements with similarities.…”

Section: Concept and Types Of Similarities 21 Introductionmentioning

confidence: 99%

Realization of an intelligent evaluation system

Maarouf

Ayachi

Bahaj

2023

IJ-ICT

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<span lang="EN-US">A number of benefits have been reported for computer-based assessments over traditional paper-based exams, both in terms of IT support for question development, reduced distribution and test administration costs, and automated support. Possible for the ranking. However, existing computerized assessment systems do not provide all kinds of questions, namely open questions that require writing solutions. To overcome the challenges of the existing, the objective of this work is to achieve an intelligent evaluation system (IES) responding to the problems identified, and which adapts to the different types of questions, especially open-ended questions of which the answer requires sentence writing or programming.</span>

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COVID-19 Candidate Treatments, a Data Analytics Approach

Wolfe

Elnashar

Schreiber

et al. 2020

2020 Fourth International Conference on Multimedia Computing, Networking and Applications (MCNA)

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COVID-19, short for "coronavirus disease 2019", has majorly affected millions of people worldwide. In the U.S. alone as of the end of this week (June 1, 2020), there have been 1,790,191 total cases, with 104,383 deaths. There have been 6,166,978 cases in the entire world, with 372,037 deaths, these are just the reported cases. Our focus in this research is in evaluating a repository of research papers to extract knowledge related to COVID-19 and possible treatments. Driven by the COVID-19 Open Research Dataset Challenge from Kaggle, we focused on a subset of that, COVID-19 Pulmonary Risks Literature Clustering. The second dataset we are using is from the Maryland Transportation Institute (MTI). The data is broken up into four categories: (1) Mobility and Social Distancing, (2) COVID and Health, (3) Economic Impact, and (4) Vulnerable Population. The data is extracted from NPR, ESRI, the COVID tracking project, CDC, and several other sources. MTI has been the source of several papers regarding mobility impact, social distancing, stayat-home orders, and non-pharmaceutical interventions.

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CORD19STS: COVID-19 Semantic Textual Similarity Dataset

Cited by 3 publications

References 23 publications

Meta-research on COVID-19: An overview of the early trends

Meta-research on COVID-19: An overview of the early trends

Realization of an intelligent evaluation system

COVID-19 Candidate Treatments, a Data Analytics Approach

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