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The system developing of forming research schools basis of publication elements analysis

Autorzy
Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this paper the method of research publications elements analysis that is determining common qualities of research publications and their clustering as an instrument of selecting and sorting out the information about research schools has been introduced. In module structuring documents transmitted there are tape that indicates the address of the file. Depending on where the file is, it can be a path to a file on the local disk or URL on the Internet.
Słowa kluczowe
Rocznik
Strony
57--66
Opis fizyczny
Bibliogr. 11 poz., fig., tab.
Twórcy
  • Lviv Polytechnic National University, Ukraine, 79013, Lviv, Bandera str., 28a
autor
  • Lviv Polytechnic National University, Ukraine, 79013, Lviv, Bandera str., 28a
Bibliografia
  • [1] Brandow R., Mitze K., And Rau L. F.: Automatic condensation of electronic publications by sentence selection. Information Processing and Management, 31 (5), 1995, pp. 675-685.
  • [2] Solton J.: Dynamic library – information systems. М: the World, 1979.
  • [3] Shakhovska N., Noha R.: One method of analysis of research publications’ elements. MEST Journal, 15 01, 2(1), 2014, pp. 94-102.
  • [4] Salton G. et al.: Automatic Text Structuring and Summarization. Information Processing & Management, vol. 33, no. 2, 1997, pp.193-207.
  • [5] Radev D. R., Mckeown K. R.: Generating Natural Language Summaries from Multiple Online Sources. Computational Linguistics, vol. 24, no. 3, 1998, pp. 469-500.
  • [6] Carbonell J.G., Goldstein J. G.: The Use of MMR, Diversity-Based Reranking for Reordering Documents and Producing Summaries. Proc. 21st Int’l ACM SIGIR Conf. Research and Development in Information Retrieval, ACM Press, New York, 1998, pp. 335-336.
  • [7] Shi Zhong: Efficient Online Spherical K-means Clustering. Proc. IEEE Int. Joint Conf. Neural Networks (IJCNN 2005), Montreal, Canada, 2005, pp. 3180-3185.
  • [8] Hou, Jun, Nayak, Richi: The heterogeneous cluster ensemble method using hubness for clustering text documents. Lecture Notes in Computer Science [Web Information Systems Engineering – WISE 2013: 14th International Conference, Nanjing, China, Proceedings, Part I], 2013, pp. 102-110.
  • [9] Strehl A., Ghosh J.: Cluster ensembles – a knowledge reuse framework for combining partitions. Journal of Machine Learning Research, no. 3, 2002, pp. 583-617.
  • [10] Strehl A., Ghosh J., Moone R. J.: Impact of similarity measures on web-page clustering. AAAI Workshop on AI for Web Search, 2002, pp. 58-64.
  • [11] Karypis G.: CLUTO – a clustering toolkit. Dept. of Computer Science, University of Minnesota, 2002. (http://www-users.cs.umn.edu/karypis/cluto)
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-37cb9314-1c31-4b17-957e-533af2787cac
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