摘要翻译:
资助研究和发展的一个关键目标一直是促进经济发展。在此基础上,发表了一系列可考虑的研究,目的是确定什么是经济影响以及如何准确衡量这种影响。许多指标被用来衡量经济影响,尽管没有一个单一指标被广泛适用。基于从Altmetric收集的专利数据,我们使用几种分类模型,通过各种社交媒体特征预测专利引用。专利引用一篇研究论文暗示了它在该领域直接应用的潜力。这些预测可供研究人员在申请专利时,对其工作的实际应用进行检测和挖掘。
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英文标题:
《Predicting Patent Citations to measure Economic Impact of Scholarly
Research》
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作者:
Abdul Rahman Shaikh and Hamed Alhoori
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最新提交年份:
2019
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分类信息:
一级分类:Computer Science 计算机科学
二级分类:Digital Libraries 数字图书馆
分类描述:Covers all aspects of the digital library design and document and text creation. Note that there will be some overlap with Information Retrieval (which is a separate subject area). Roughly includes material in ACM Subject Classes H.3.5, H.3.6, H.3.7, I.7.
涵盖了数字图书馆设计和文献及文本创作的各个方面。注意,与信息检索(这是一个单独的主题领域)会有一些重叠。大致包括ACM课程H.3.5、H.3.6、H.3.7、I.7中的材料。
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一级分类:Computer Science 计算机科学
二级分类:Machine Learning 机器学习
分类描述:Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文(有监督的,无监督的,强化学习,强盗问题,等等),包括健壮性,解释性,公平性和方法论。对于机器学习方法的应用,CS.LG也是一个合适的主要类别。
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一级分类:Economics 经济学
二级分类:General Economics 一般经济学
分类描述:General methodological, applied, and empirical contributions to economics.
对经济学的一般方法、应用和经验贡献。
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一级分类:Quantitative Finance 数量金融学
二级分类:Economics 经济学
分类描述:q-fin.EC is an alias for econ.GN. Economics, including micro and macro economics, international economics, theory of the firm, labor economics, and other economic topics outside finance
q-fin.ec是econ.gn的别名。经济学,包括微观和宏观经济学、国际经济学、企业理论、劳动经济学和其他金融以外的经济专题
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英文摘要:
A crucial goal of funding research and development has always been to advance economic development. On this basis, a consider-able body of research undertaken with the purpose of determining what exactly constitutes economic impact and how to accurately measure that impact has been published. Numerous indicators have been used to measure economic impact, although no single indicator has been widely adapted. Based on patent data collected from Altmetric we predict patent citations through various social media features using several classification models. Patents citing a research paper implies the potential it has for direct application inits field. These predictions can be utilized by researchers in deter-mining the practical applications for their work when applying for patents.
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PDF链接:
https://arxiv.org/pdf/1906.08244