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Using of Linguistic Analysis of Search Query for Improving the Quality of Information Retrieval

  • Nadezhda Yarushkina
  • Aleksey FilippovEmail author
  • Maria Grigoricheva
Conference paper
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 199)

Abstract

The paper describes the process of research and development of methods for linguistic analysis of search queries. Linguistic analysis of search query is used to improve the quality of information retrieval. After syntactic analysis of original search query it translated to a search query in a new format. Taking into account, the features of information retrieval query language allow improving the quality of information retrieval. Also, the paper describes the results of experiments that confirm the correctness of the method.

Keywords

Information retrieval Syntactic analysis Search queries 

Notes

Acknowledgments

The study was supported by:

the Ministry of Education and Science of the Russian Federation in the framework of the project No. ~2.1182.2017/4.6. Development of methods and means for automation of production and technological preparation of aggregate-assembly aircraft production in the conditions of a multi-product production program;

the Russian Foundation for Basic Research (Grants No. 18-47-730035 and 16-47-732054).

References

  1. 1.
    Voorhees, E.M.: Natural language processing and information retrieval. In: Information Extraction, pp. 32–48. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  2. 2.
    Manning, C., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press, Cambridge (2008)Google Scholar
  3. 3.
    Crammer, K., Dekel, O., Keshet, J., Shalev-Shwartz, S., Singer, Y.: Online passive-aggressive algorithms. JMLR 7, 551–585 (2006)Google Scholar
  4. 4.
    Turney, P.: Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the Association for Computational Linguistics, pp. 417–424 (2002)Google Scholar
  5. 5.
    VKontakte. https://vk.com/. Accessed 20 Oct 2018
  6. 6.
    Gruber, T.: Ontology. http://tomgruber.org/writing/ontology-in-encyclopedia-of-dbs.pdf. Accessed 20 Oct 2018
  7. 7.
    Elasticsearch. https://www.elastic.co/. Accessed 20 Oct 2018
  8. 8.
    MongoDB. https://www.mongodb.com/. Accessed 20 Oct 2018
  9. 9.
    Neo4j. https://neo4j.com/. Accessed 20 Oct 2018
  10. 10.
    Yarushkina, N., Filippov, A., Moshkin, V.: Development of the unified technological platform for constructing the domain knowledge base through the context analysis. In: Creativity in Intelligent Technologies and Data Science, pp. 62–72 (2017)Google Scholar
  11. 11.
  12. 12.
    SRILM—The SRI Language Modeling Toolkit. http://www.speech.sri.com/projects/srilm. Accessed 20 Oct 2018
  13. 13.
    Manning, C., Schutze, H.: Foundations of Statistical Language processing. The MIT Press, Cambridge (1999)Google Scholar
  14. 14.
    Sboev, A.G., Gudovskikh, D.V., Ivanov, I., Moloshnikov, I.A., Rybka, R.B., Voronina, I.: Research of a Deep Learning Neural Network Effectiveness for a Morphological Parser of Russian Language (2017). http://www.dialog-21.ru/media/3944/sboevagetal.pdf. Accessed 20 Oct 2018
  15. 15.
    Ulyanovsk. https://en.wikipedia.org/wiki/Ulyanovsk. Accessed 20 Oct 2018

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Ulyanovsk State Technical UniversityUlyanovskRussian Federation

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