Moscow, Russian Federation
Russian Federation
OKSO 45.03.03 Фундаментальная и прикладная лингвистика
The object of the research when writing the work was the body of text data collected together with the scientific advisor and the algorithms for processing the natural language of analysis. The stream of hypotheses has been tested against computer science scientific publications through a series of simulation experiments described in this dissertation. The subject of the research is algorithms and the results of the algorithms, aimed at predicting promising topics and terms that appear in the course of time in the scientific environment. The result of this work is a set of machine learning models, with the help of which experiments were carried out to identify promising terms and semantic relationships in the text corpus. The resulting models can be used for semantic processing and analysis of other subject areas.
citation, impact factor, scientific publications, machine learning
1. Bethard S., Jurafsky D. Who should I cite: learning literature search models from citation behavior. In Proceedings of the 19th ACM international conference on Information and knowledge management, pages 609-618. ACM, 2010.
2. Pan, R. K. & Fortunato, S. (2014). Author Impact Factor: tracking the dynamics of individual scientific impact. Scientific reports, 4, 4880. (Impakt-faktor avtora: otslezhivanie dinamiki individual'nogo nauchnogo vozdeystviya).
3. Petersen, A. M. et al. Reputation and Impact in Academic Careers arXiv:1303.7274.
4. Web of knowledge. http://wokinfo.com/ (2014). Accessed: 2014-02-01.
5. Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, Jeffrey Dean. Distributed Representations of Words and Phrases and their Compositionality (2013) https://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compo.
6. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin. Attention Is All You Need https://arxiv.org/abs/1706.03762.
7. Yan R., Huang C., Tang J., Zhang Y., Li X. To Better Stand on the Shoulder of Giants, JCDL. 2012. Available at: http://keg.cs.tsinghua.edu.cn/jietang/publications/ JCDL12-Yan-et-al-To-Better-Stand-on-the-Shoulderof-Giants.pdf.
8. Ho Qirong, Eisenstein J., Xing E. P. 2012. Document hierarchies from text and links. In Proceedings of the 21st international conference on World Wide Web, pages 739-748.
9. Nicolaisen J. Citation Analysis // Annual Review of Information Science and Technology. - 2007. - Vol. 41, No.1. - P. 609-641. -http://doi.org/10.1002/aris.2007. 1440410120.
10. Seglen P. O. Why the impact factor of journals should not be used for evaluating research// BMJ: British Medical Journal. - 1997.- No. 314 (February).- P. 498-502. - http://dx.doi.org/10.1136/bmj.314.7079.497.
11. Knot, P., Germannova, D. Na puti k semantometriyam: novyy semanticheskiy kriteriy shodstva dlya ocenki vklada nauchnoy publikacii. Mezhdunar. forum po inform. 2015. T. 40. № 1, s. 3-8.
12. Porter A. L., Rafols I. 2009. Is science becoming more interdisciplinary? Measuring and mapping six research fields over time. Scientometrics 81, 3: 719-745. http://doi.org/10.1007/s11192-008-2197-2.
13. Hofmann D., Cohn T. 2001. The missing link a probabilistic model of document content and hypertext connectivity. In Proceedings of the 2000 Conference on Advances in Neural Information Processing Systems. The MIT Press, pages 430-436.
14. Erosheva E., Fienberg S., Lafferty J. 2004. Mixedmembership models of scientific publications. Proceedings of the National Academy of Sciences of the United States of America, 101(Suppl.1):5220-5227.
15. Shi X., Tseng B., Adamic L. Information Diffusion in Computer Science Citation Networks. arXiv:0905.2636v1 [cs.DL] 15 May 2009.
16. PobiedinaN.,IchiseR.PredictingCitationCountsfor Academic Literature Using Graph Pattern Mining. International Conference on Industrial, Engineering and Other Applications of Applied Intelligent SystemsIEA/AIE2014:ModernAdvancesinApplied Intelligence.
17. Priem J., Taraborelli D., Groth P., Neylon C. Altmetrics: A manifesto, 2010. http://altmetrics.org/manifesto/.
18. Sutherland W. J., Goulson D., Potts S. G., Dicks L. V. Quantifying the impact and relevance of scientific research. PLoS ONE, 6(11), 2011. http://doi.org/10.1371/journal.pone.0027537.
19. Whalen R., Huang Y. et al. Citation distance: Measuring changes in scientific search strategies. In Proceedings of the 25th International Conference Companion on World Wide Web, WWW ’16 Companion, pages 419-423, Republic and Canton of Geneva, Switzer.
20. Porter A. L., Rafols I. 2009. Is science becoming more interdisciplinary? Measuring and mapping six research fields over time. Scientometrics 81, 3: 719-745. http://doi.org/10.1007/s11192-008-2197-2.
21. Shiji Chen, Cl´ement Arsenault, Vincent Larivi`ere. 2015. Are top-cited papers more interdisciplinary? Journal of Informetrics 9, 4: 1034-1046. http://doi.org/10.1016/j.joi.2015.09.003.
22. Alam H., Kumar A., Werner T., Vyas M. Are Cited References Meaningful? Measuring Semantic Relatedness in Citation Analysis. In: Proc. of the 2nd Joint Workshop on Bibliometricenhanced Information Retrieval and Natural Language Processing for Digital.