Advertisement

Improved Approach to Extract Knowledge from Unstructured Data Using Applied Natural Language Processing Techniques

  • U. MahenderEmail author
  • M. Kumara Swamy
  • Hafeezuddin Shaik
  • Sheo Kumar
Conference paper
  • 18 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1090)

Abstract

Extraction of meaningful knowledge from a unstructured data is a complex task. In the literature, efforts have been made using text mining approaches. These approaches employ rich amount of resources in mining the textual datasets. In this paper, we focus on optimization of mining algorithm of text data to generate the automatic text summarization. In this approach, we extract text summaries from the text data carpus using natural language processing techniques. We propose a mining approach in semantic parsing and generate the automatic text summaries. We conduct the experiments on the real-world dataset and show the proposed approach is useful than the existing approaches.

Keywords

Data mining Natural language processing Text mining Summarization 

References

  1. 1.
    Murty, M. Ramakrishna, J.V.R. Murthy, P.V.G.D. Prasad Reddy, and Suresh Chandra Satapathy. 2012. A survey of cross-domain text categorization techniques. In RAIT 2012, 499–504.Google Scholar
  2. 2.
    Radev, D.R., E. Hovy, and K. McKeown. 2002. Introduction to the Special Issue on Summarization. Computational Linguistics 28 (4): 399–408.CrossRefGoogle Scholar
  3. 3.
    Turpin, A., Y. Tsegay, D. Hawking, and H. E. Williams. 2007. Fast Generation of Result Snippets in Web Search. In Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM, 127–134.Google Scholar
  4. 4.
    Lahari, K., M. Ramakrishna Murty. 2015. Partition Based Clustering Using Genetic Algorithms and Teaching Learning Based Optimization: Performance Analysis. In International Conference and Published the Proceedings in AISC, vol. 2, 191–200. Berlin: Springer.  https://doi.org/10.1007/978-3-319-13731-5_22.
  5. 5.
    Ricardo Baeza-Yates, and Ribeiro-Neto Berthier. 2011. Modern Information Retrieval: The Concepts and Technology Behind Search, 2nd ed. USA: Addison-Wesley Publishing Company.Google Scholar
  6. 6.
    Edmundson, Harold P. 1969. New Methods in Automatic Extracting. Journal of the ACM 16 (2): 264–285.CrossRefGoogle Scholar
  7. 7.
    Allahyari Mehdi, Seyedamin Pouriyeh, Mehdi Assefi, Safaei Saeid, Elizabeth D. Trippe, Juan B. Gutierrez, and Krys Kochut. 2017. Text Summarization Techniques: A Brief Survey. International Journal of Advanced Computer Science and Applications 8 (10): 397–405.CrossRefGoogle Scholar
  8. 8.
    Luhn, Hans Peter. 1958. The Automatic Creation of Literature Abstracts. IBM Journal of Research and Development 2 (2): 159–165.MathSciNetCrossRefGoogle Scholar
  9. 9.
    Rabinowitz, P. 1980. On Subharmonic Solutions of a Hamiltonian System. Communications on Pure Applied Mathematics 33: 609–633.MathSciNetCrossRefGoogle Scholar
  10. 10.
    Clarke, F., and I. Ekeland. 1982. Nonlinear Oscillations and Boundary-Value Problems for Hamiltonian Systems. Archive Rational Mechanics and Analysis 78: 315–333.MathSciNetCrossRefGoogle Scholar
  11. 11.
    Clarke F., I. Ekeland. 1978. Solutions periodiques, duperiode donnee, des equations hamiltoniennes. Note CRAS Paris 287, 1013–1015.Google Scholar
  12. 12.
    Michalek, R., and G. Tarantello. 1988. Subharmonic Solutions with Prescribed Minimal Period for Nonautonomous Hamiltonian Systems. Journal of Differential Equations 72: 28–55.MathSciNetCrossRefGoogle Scholar
  13. 13.
    Tarantello, G. (to appear). Subharmonic solutions for Hamiltonian systems via a ZZp pseudoin-dex theory. Annali di Matematica Pura .Google Scholar
  14. 14.
    Allahyari, M., S. Pouriyeh, M. Assefi, S. Safaei, E. D. Trippe, J. B. Gutierrez, and K. Kochut. 2017. A Brief Survey of Text Mining: Classification, Clustering and Extraction Techniques. ArXiv e-prints.Google Scholar
  15. 15.
    Manjusha, K., M. Anand Kumar, and K.P. Soman. 2017. Reduced Scattering Representation for Malayalam Character Recognition. Arabian Journal for Science and Engineering.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • U. Mahender
    • 1
    Email author
  • M. Kumara Swamy
    • 1
  • Hafeezuddin Shaik
    • 2
  • Sheo Kumar
    • 1
  1. 1.Department of Computer Science & EngineeringCMR Engineering CollegeHyderabadIndia
  2. 2.HyderabadIndia

Personalised recommendations