Semantic-Based Linguistic Platform for Big Data Processing

  • A. Bobkov
  • S. Gafurov
  • Viktor KrasnoproshinEmail author
  • H. Vissia
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1055)


The paper deals with the development of a semantic-based linguistic platform. Special attention is paid to semantic patterns.


Big data Natural language processing Semantic patterns Ontology-based approach 


  1. 1.
    Simon, Ph.: Too Big to Ignore: The Business Case for Big Data, 256 p. Wiley, Hoboken (2015)Google Scholar
  2. 2.
    Davenport, Th.: Big Data at Work: Dispelling the Myths, Uncovering the Opportunities, 228 p. Harvard Business Review Press, Boston (2014)Google Scholar
  3. 3.
    Mayer-Schönberger, V., Cukier, K.: Big Data: A Revolution that will Transform How We Live, Work, and Think, 242 p. Houghton Mifflin Harcourt, Boston (2013)Google Scholar
  4. 4.
    Marr, B.: Big Data - Using SMART Big Data, Analytics and Metrics to Make Better Decisions and Improve Performance, 256 p. Wiley, Hoboken (2015)Google Scholar
  5. 5.
    Marr, B.: Data Strategy: How to Profit from a World of Big Data, Analytics and the Internet of Things, 200 p. Kogan Page, London (2017)Google Scholar
  6. 6.
    Moens, M.: Information Extraction: Algorithms and Prospects in a Retrieval Context, 246 p. Springer, Berlin (2006)Google Scholar
  7. 7.
    Baeza-Yates, R., Ribeiro-Neto, B.: Modern Information Retrieval: the Concepts and Technology Behind Search. Addison-Wesley Professional, Boston, 944 p. (2011)Google Scholar
  8. 8.
    Buettcher, S., Clarke, C., Cormack, G.: Information Retrieval: Implementing and Evaluating Search Engines, 632 p. MIT Press, Cambridge (2010)Google Scholar
  9. 9.
    Machová, K., Bednár, P., Mach, M.: Various approaches to web information processing. Comput. Inf. 26, 301–327 (2007)zbMATHGoogle Scholar
  10. 10.
    Khoo, C., Myaeng, S.H.: Identifying semantic relations in text for information retrieval and information extraction. In: Green, R., Bean, C.A., Myaeng, S.H. (eds.) The Semantics of Relationships. Information Science and Knowledge Management. ISKM, vol. 3, pp. 161–180. Springer, Berlin (2002). Scholar
  11. 11.
    Bobkov, A., Gafurov, S., Krasnoproshin, V., Romanchik, V., Vissia, H.: Information extraction based on semantic patterns. In: Proceedings of the 12-th International Conference – PRIP 2014, Minsk, pp. 30–35 (2014)Google Scholar
  12. 12.
    Barnbrook, G., Mason, O., Krishnamurthy, R.: Collocation: Applications and Implications, 254 p. Palgrave Macmillan, London (2013)CrossRefGoogle Scholar
  13. 13.
    Cruse, D.A.: Lexical Semantics, 310 p. Cambridge University Press, Cambridge (1986)Google Scholar
  14. 14.
    Manning, C.D., Schütze, H.: Foundations of Statistical Natural Language Processing, 620 p. MIT Press, Cambridge (1999)Google Scholar
  15. 15.
    Bilan, V., Bobkov, A., Gafurov, S., Krasnoproshin, V., van de Laar, J., Vissia, H.: An ontology-based approach to opinion mining. In: Proceedings of 10-th International Conference PRIP 2009, Minsk, pp. 257–259 (2009)Google Scholar
  16. 16.
    Fensel, D.: Foundations for the Web of Information and Services: A Review of 20 Years of Semantic Web Research, 416 p. Springer, Berlin (2011)Google Scholar
  17. 17.
  18. 18.
    Pang, B., Lee, L.: Opinion Mining and Sentiment Analysis, 148 p. Now Publishers Inc., Boston (2008)CrossRefGoogle Scholar
  19. 19.
    Devitt, A., Ahmad, K.: Sentiment analysis in financial news: a cohesion-based approach. In: Proceedings of the Association for Computational Linguistics (ACL 2007), pp. 984–991 (2007)Google Scholar
  20. 20.
    Eguchi, K., Lavrenko, V.: Sentiment retrieval using generative models. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2006), pp. 345–354 (2006)Google Scholar
  21. 21.
    Clark, A.V.: Mood State and Health, 213 p. Nova Publishers, Boston (2005)Google Scholar
  22. 22.
    McNair, D.M, Lorr, M., Droppleman, L.F.: Profile of Mood States - San Diego. Educational and Industrial Testing Service, California (1971)Google Scholar
  23. 23.
    Siegel, E.: Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, 320 p. Wiley, Hoboken (2013)Google Scholar
  24. 24.
    Mishra, N.: Predictive analytics: a survey, trends, applications, opportunities & challenges. Int. J. Comput. Sci. Inf. Technol. 3, 4434–4438 (2012)Google Scholar
  25. 25.
    Asghar, N.: Automatic extraction of causal relations from natural language texts: a comprehensive survey. arXiv preprint arXiv:1605.07895, May 2016
  26. 26.
    Sorgente, A.: Automatic extraction of cause-effect relations in natural language text. In: Proceedings of the 13th Conference of the Italian Association for Artificial Intelligence, pp. 37–48 (2013)Google Scholar
  27. 27.
    Darian, S.: Cause and effect in a corpus of science textbooks. ESP. Malaysia 4, 65–83 (1996)Google Scholar
  28. 28.
    Khoo, C.S.G.: Automatic identification of causal relations in text and their use for improving precision in information retrieval. Doctoral dissertation, Syracuse University (1995). Dissertation Abstracts International, 5704A, 1364Google Scholar
  29. 29.
    Xuelan, F., Kennedy, G.: Expressing causation in written English. RELC J. 23(1), 62–80 (1992)CrossRefGoogle Scholar
  30. 30.
    Chan, K., Lam, W.: Extracting causation knowledge from natural language texts. Int. J. Intell. Syst. 20(3), 327–358 (2005)CrossRefGoogle Scholar
  31. 31.
    Radinsky, K., Davidovich, S.: Learning to predict from textual data. J. Artif. Intell. Res. 45, 641–684 (2012)CrossRefGoogle Scholar
  32. 32.
    Radinsky, K.: Learning causality for news events prediction. In: Proceedings of the 21st International Conference on World Wide Web ACM, pp. 909–918 (2012)Google Scholar
  33. 33.
    Kaplan, R., Berry-Rogghe, G.: Knowledge-based acquisition of causal relationships in text. Knowl. Acquisition 3(3), 317–337 (1991)CrossRefGoogle Scholar
  34. 34.
    Wolff, P., Song, G., Driscoll, D.: Models of causation and causal verbs. In: Meeting of the Chicago Linguistics Society, main session, vol. 1, p. 607–622 (2002)Google Scholar
  35. 35.
    Levin, B., Hovav, M.A.: Preliminary analysis of causative verbs in English. Lingua 92, 35–77 (1994)CrossRefGoogle Scholar
  36. 36.
    Altenberg, B.: Causal linking in spoken and written English. Studia Linguistica 38(1), 20–69 (1984)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • A. Bobkov
    • 1
  • S. Gafurov
    • 2
  • Viktor Krasnoproshin
    • 1
    Email author
  • H. Vissia
    • 2
  1. 1.Belarusian State UniversityMinskRepublic of Belarus
  2. 2.ByeleX BVOud GastelThe Netherlands

Personalised recommendations