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Detection of Domain-Specific Trends in Text Collections

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Book cover Analysis of Images, Social Networks and Texts (AIST 2014)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 436))

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Abstract

This study considers the problem of automatic trend detection in document collections related to several specific domains. The suggested trend detection algorithm is based on the domain-specific trend model. The algorithm was evaluated on documents from shipbuilding and power engineering domains.

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Notes

  1. 1.

    The code, instructions and results can be found on https://bitbucket.org/ilnurgadelshin/trends.

  2. 2.

    The full lists of the found trends could be found on https://bitbucket.org/ilnurgadelshin/trends.

References

  1. Porter, F.: An algorithm for suffix stripping. Program 14(3), 130–137 (1980)

    Article  Google Scholar 

  2. Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Inf. Process. Manage. 24(5), 513–523 (1988)

    Article  Google Scholar 

  3. Natural Language Toolkit. http://www.nltk.org/

  4. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)

    MATH  Google Scholar 

  5. Vorontsov, K.V., Potapenko, A.A.: Regularization, robustness and sparseness of probabilistic topic models. Comput. Res. Model. 2, 161–174 (2012) (in Russian)

    Google Scholar 

  6. Teh, Y.W., Jordan, M.I.: Hierarchical Bayesian nonparametric models with applications. In: Hjort, N., Holmes, C., Müller, P., Walker, S. (eds.) Bayesian Nonparametrics Principles and Practice. Cambridge University Press, Cambridge (2009)

    Google Scholar 

  7. Teh, Y.W.: Dirichlet processes. In: Sammut, C., Webb, G.I. (eds.) Encyclopedia of Machine Learning, pp. 280–287. Springer, Heidelberg (2010)

    Google Scholar 

  8. Blei, D, Lafferty, J.: Dynamic topic models. In: ICML (2006)

    Google Scholar 

  9. Glance, N., Hurst, M., Tomokiyo, T.: BlogPulse: automated trend discovery for weblogs. In: WWW 2004, ACM (2004)

    Google Scholar 

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Authors and Affiliations

  1. Higher School of Economics, Moscow, Russia

    Ilnur Gadelshin, Anna Antonova & Dmitry Ilvovsky

Authors
  1. Ilnur Gadelshin
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  2. Anna Antonova
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  3. Dmitry Ilvovsky
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Corresponding author

Correspondence to Dmitry Ilvovsky .

Editor information

Editors and Affiliations

  1. National Research University Higher School of Economics, Moscow, Russia

    Dmitry I. Ignatov

  2. Krasovsky Inst. of Math. and Mechanics, Yekaterinburg, Russia

    Mikhail Yu. Khachay

  3. Université catholique de Louvain, Moscow, Russia

    Alexander Panchenko

  4. University of Wolverhampton, Wolverhampton, United Kingdom

    Natalia Konstantinova

  5. National Research University, Moscow, Russia

    Rostislav E. Yavorsky

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© 2014 Springer International Publishing Switzerland

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Gadelshin, I., Antonova, A., Ilvovsky, D. (2014). Detection of Domain-Specific Trends in Text Collections. In: Ignatov, D., Khachay, M., Panchenko, A., Konstantinova, N., Yavorsky, R. (eds) Analysis of Images, Social Networks and Texts. AIST 2014. Communications in Computer and Information Science, vol 436. Springer, Cham. https://doi.org/10.1007/978-3-319-12580-0_7

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  • DOI: https://doi.org/10.1007/978-3-319-12580-0_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12579-4

  • Online ISBN: 978-3-319-12580-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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