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Sentence Completion Using Text Prediction Systems

  • Kavita Asnani
  • Douglas Vaz
  • Tanay PrabhuDesai
  • Surabhi Borgikar
  • Megha Bisht
  • Sharvari Bhosale
  • Nikhil Balaji
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 327)

Abstract

Text Prediction for sentence completion is a widely used method to enhance the speed of communication as well as reducing the total time taken to compose text. This paper briefly describes the approaches, design and implementation issues involved, as well as the factors and parameters that determine effectiveness of a system. The information is then used to build a software system, capable of modeling text data, in order to generate predictions in real-time. By using a pure statistical approach, we generate N-gram models that are adaptive to users by applying instance based learning. Details of the software development method, used to prototype and iteratively build a highly effective system, are provided.

Keywords

text prediction statistical NLP N-gram model 

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References

  1. 1.
    Bickel, S., Haider, P., Scheffer, T.: Learning to complete sentences. In: Gama, J., Camacho, R., Brazdil, P.B., Jorge, A.M., Torgo, L. (eds.) ECML 2005. LNCS (LNAI), vol. 3720, pp. 497–504. Springer, Heidelberg (2005)Google Scholar
  2. 2.
    Zagler, W., Beck, C.: FASTY - faster typing for disabled persons. In: Proceedings of the European Conference on Medical and Biological Engineering (2002)Google Scholar
  3. 3.
    Garay-Vitoria, N., Abascal, J.: Text prediction systems: a survey. Universal Access in the Information Society 4(3), 188–203 (2006)CrossRefGoogle Scholar
  4. 4.
    Garay-Vitoria, N., Abascal, J.: A Comparison of Prediction Techniques to Enhance the Communication Rate. In: Stary, C., Stephanidis, C. (eds.) UI4ALL 2004. LNCS, vol. 3196, pp. 400–417. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  5. 5.
    Baker, L.D., McCallum, A.K.: Distributional clustering of words for text classification. In: Proceedings of the 21st ACM-SIGIR International Conference on Research and Development in Information Retrieval (SIGIR) (1998)Google Scholar
  6. 6.
    Rosenfeld, R.: Two decades of statistical language modeling: Where do we go from here? Proceedings of the IEEE 88 (2000)Google Scholar
  7. 7.
    Rosenfeld, R.: Adaptive statistical language modeling: a maximum entropy approach. Diss. IBM (2005)Google Scholar
  8. 8.
    Hecht-Nielsen, R.: Confabulation Theory: The Mechanism of Thought. Springer (August 2007)Google Scholar
  9. 9.
    Qiu, Q., et al.: Confabulation based sentence completion for machine reading. In: 2011 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB). IEEE (2011)Google Scholar
  10. 10.
    Brants, T., Popat, A.C., Xu, P., Och, F.J., Dean, J.: Large language models in machine translation. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (2007)Google Scholar
  11. 11.
    Manning, C.D., Schütze, H.: Foundations of Statistical Natural Language Processing. MIT Press (1999) ISBN 0-262-13360-1Google Scholar
  12. 12.
    British Academic Written English (BAWE) corpus, Hilary Nesi, Sheena Gardner (Centre for Applied Linguistics, Warwick), Paul Thompson (Department of Applied Linguistics, Reading) and Paul Wickens (Westminster Institute of Education, Oxford Brookes)Google Scholar
  13. 13.
    Chen, S.F., Goodman, J.: An Empirical Studies of Smoothing Techniques for Language Modelling. TR-10-98 August (1998)Google Scholar
  14. 14.
    Magnuson, T., Hunnicutt, S.: Measuring the effectiveness of word prediction: The advantage of long-term use. Speech, Music and Hearing 43, 57–67 (2002)Google Scholar
  15. 15.
    Pauls, A., Klein, D.: Faster and smaller n-gram language models. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, vol. 1. Association for Computational Linguistics (2011)Google Scholar
  16. 16.
    Oxford English Corpus: Facts about the language. Oxford University PressGoogle Scholar
  17. 17.
    Shannon, C.E.: A mathematical theory of communication. The Bell System Technical Journal 27(3), 379–423 (1948)MathSciNetCrossRefMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Kavita Asnani
    • 1
  • Douglas Vaz
    • 1
  • Tanay PrabhuDesai
    • 1
  • Surabhi Borgikar
    • 1
  • Megha Bisht
    • 1
  • Sharvari Bhosale
    • 1
  • Nikhil Balaji
    • 1
  1. 1.Department of Computer EngineeringPadre Conceição College of EngineeringGoaIndia

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