A vector space model for automatic indexing
Communications of the ACM
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Supervised Machine Learning: A Review of Classification Techniques
Proceedings of the 2007 conference on Emerging Artificial Intelligence Applications in Computer Engineering: Real Word AI Systems with Applications in eHealth, HCI, Information Retrieval and Pervasive Technologies
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Indicators based on non-patent references (NPRs) are increasingly being used for measuring and assessing science---technology interactions. But NPRs in patent documents contain noise, as not all of them can be considered `scientific'. In this article, we introduce the results of a machine-learning algorithm that allows identifying scientific references in an automated manner. Using the obtained results, we analyze indicators based on NPRs, with a focus on the difference between NPR- and scientific non-patent references-based indicators. Differences between both indicators are significant and dependent on the considered patent system, the applicant country and the technological domain. These results signal the relevancy of delineating scientific references when using NPRs to assess the occurrence and impact of science---technology interactions.