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Bibliometric analysis of the world scientific production in Chemical Engineering during 2000-2011. Part 2: Analysis of the 1,000 most cited publications
A comprehensive bibliometric analysis of the scientific production of Ch...
12/31/2020 ∙ by Ruben Miranda, et al. ∙
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Etymo: A New Discovery Engine for AI Research
We present Etymo (https://etymo.io), a discovery engine to facilitate ar...
01/25/2018 ∙ by Weijian Zhang, et al. ∙
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Identifying the Development and Application of Artificial Intelligence in Scientific Text
We describe a strategy for identifying the universe of research publicat...
02/17/2020 ∙ by James Dunham, et al. ∙
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An Explorative Study of GitHub Repositories of AI Papers
With the rapid development of AI technologies, thousands of AI papers ar...
02/16/2019 ∙ by Boyang Zhang, et al. ∙
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On Quantifying and Understanding the Role of Ethics in AI Research: A Historical Account of Flagship Conferences and Journals
Recent developments in AI, Machine Learning and Robotics have raised con...
09/21/2018 ∙ by Marcelo Prates, et al. ∙
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paper2repo: GitHub Repository Recommendation for Academic Papers
GitHub has become a popular social application platform, where a large n...
04/13/2020 ∙ by Huajie Shao, et al. ∙
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Semantic Search in Millions of Equations
Given the increase of publications, search for relevant papers becomes t...
10/27/2020 ∙ by Katharina Morik, et al. ∙
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Increasing Papers' Discoverability with Precise Semantic Labeling: the sci.AI Platform
05/02/2017 ∙ by Roman Gurinovich, et al. ∙
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The number of published findings in biomedicine increases continually. At the same time, specifics of the domain's terminology complicates the task of relevant publications retrieval. In the current research, we investigate influence of terms' variability and ambiguity on a paper's likelihood of being retrieved. We obtained statistics that demonstrate significance of the issue and its challenges, followed by presenting the sci.AI platform, which allows precise terms labeling as a resolution.
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