Efficient supervised and semi-supervised approaches for affiliations disambiguation
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DOI: 10.1007/s11192-013-1025-5
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- Fernanda Morillo & Belén Álvarez-Bornstein, 2018. "How to automatically identify major research sponsors selecting keywords from the WoS Funding Agency field," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(3), pages 1755-1770, December.
- Ali Daud & Muhammad Ahmad & M. S. I. Malik & Dunren Che, 2015. "Using machine learning techniques for rising star prediction in co-author network," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(2), pages 1687-1711, February.
- Andrea Ancona & Roy Cerqueti & Gianluca Vagnani, 2023. "A novel methodology to disambiguate organization names: an application to EU Framework Programmes data," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(8), pages 4447-4474, August.
- Nicolás Robinson-García & Clara Calero-Medina, 2014. "What do university rankings by fields rank? Exploring discrepancies between the organizational structure of universities and bibliometric classifications," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(3), pages 1955-1970, March.
- Maxim Kotsemir & Sergey Shashnov, 2017. "Measuring, analysis and visualization of research capacity of university at the level of departments and staff members," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(3), pages 1659-1689, September.
- Deyun Yin & Kazuyuki Motohashi & Jianwei Dang, 2020. "Large-scale name disambiguation of Chinese patent inventors (1985–2016)," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(2), pages 765-790, February.
- Vittorio Fuccella & Domenico De Stefano & Maria Prosperina Vitale & Susanna Zaccarin, 2016. "Improving co-authorship network structures by combining multiple data sources: evidence from Italian academic statisticians," Scientometrics, Springer;Akadémiai Kiadó, vol. 107(1), pages 167-184, April.
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Keywords
Affiliation; Disambiguation; Data cleaning; Classification; Clustering; Semi-supervised; Bibliographic databases; K-means; Naive bayes;All these keywords.
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