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Cognitive Semiotic Model for Query Expansion in Question Answering

  • Alexander SirenkoEmail author
  • Galina Cherkasova
  • Yuriy Philippovich
  • Yuriy Karaulov
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 436)

Abstract

Query expansion improves performance of informational retrieval stage in question answering pipeline. We state the benefits of a personalized and autonomous query preprocessing and automate a semiotic model to achieve such properties. The model operates as a context-sensitive weighted grammar, along with the algorithm to apply production rules allowing approximate matching. The semiotic model is packed into a regression model to predict relevant terms for a query. ROC-analysis evaluates the regression model and helps to choose the optimal cutoff level. We compare ranking of terms by regression model and ranking based on an external informational retrieval system.

Keywords

Semiotic modeling Cognitive experiments Grammar Query expansion Regression Question answering 

Notes

Acknowledgements

The research is supported by the grant №12-04-12039v of the Russian Humanitarian Scientific Fund and the grant №NSH-5740.2014.6 from the President of Russia.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Alexander Sirenko
    • 1
    • 4
    Email author
  • Galina Cherkasova
    • 2
    • 4
  • Yuriy Philippovich
    • 3
    • 4
  • Yuriy Karaulov
    • 3
    • 4
  1. 1.Moscow State University of Printing ArtsMoscowRussia
  2. 2.The Institute of LinguisticsRussian Academy of SciencesMoscowRussia
  3. 3.Bauman Moscow State Technical UniversityMoscowRussia
  4. 4.The V.V. Vinogradov Russian Language Institute of the Russian Academy of SciencesMoscowRussia

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