The Use of Psycholinguistic Patterns in Interactive Systems of Active Information Retrieval

  • Authors

    • Valery Evgenevich Sachkov
    • Dmitry Aleksandrovich Akimov
    • Sergey Aleksandrovich Pavelyev
    2018-12-03
    https://doi.org/10.14419/ijet.v7i4.38.24595
  • Active Search, AIML, Dialog systems, Psycholinguistic patterns, RDF, Semantic core.

  • The article explores the possibility of using psycholinguistic patterns in a dialogue with the Internet visitors. The scheme of the semantic kernel is shown for the purpose-setting installation of the search system and the methodology for constructing patterns, taking into account the psycholinguistic features of constructing a dialogue for obtaining the required information. The model of building psycholinguistic patterns for revealing the semantic information in dialogues is given. Patterns are based on associative links of words and word combinations. Such associative connections allow expanding the list of related words and revealing key information in the best way from short messages. The use of such a method in interactive active search systems makes it possible to improve information exchange and achieve a higher level of identifying the purpose of the dialogue.

     

     

     

  • References

    1. [1] Marietto M das GB, de Aguiar RV, Barbosa G de O, Botelho WT, Pimentel E, França R dos S & da Silva VL (2013), Artificial Intelligence Markup Language: A Brief Tutorial. International Journal of Computer science and engineering Survey 4(3), 1-20.

      [2] Liu C-W, Lowe R, Serban IV, Noseworthy M, Charlin L & Pineau J (2016), How NOT to evaluate your dialogue system: An empirical study of unsupervised evaluation metrics for dialogue response generation. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. Austin, Texas, 2122-2132.

      [3] Sachkov VE, Gilmutdinova EF, Matyash ED & Akimov DA (2016), Processing and computer analysis of the text in natural languages. Journal of Contemporary Science: Actual Problems of Theory and Practice, Series of Natural and Technical Sciences 12, 57-64.

      [4] Colbaugh R & Glass K (2013), Analyzing social media content for security informatics. Proceeding of European Intelligence and Security Informatics Conference, Uppsala, Sweden, 45–51.

      [5] Weizenbaum J (1976), Computer Power and Human Reason: From Judgment to Calculation. New York: Freeman and Company.

      [6] Wallace RS (2009), The Anatomy of A.L.I.C.E. In: Epstein R, Roberts G & Beber, G (Eds) Parsing the Turing Test. Springer, 181-210.

      [7] Serban IV, Sordoni A., Bengio Y., Courville A., Pineau J (2016), Building End-To-End Dialogue Systems Using the Generative Hierarchical Neural Network Models. Proceedings of the AAAI'16 Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence. Phoenix, Arizona, 3776-3783.

      [8] Henderson M, Thomson B & Williams J (2014), The Second Dialog State Tracking Challenge. Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL). Philadelphia, PA, 263-272.

      [9] Resource Description Environment (RDF) (2004), Concepts and Abstract Syntax. The World Wide Web Consortium (W3C), 10 February. https://www.w3.org/TR/rdf11-concepts/

      [10] W3C Semantic web activity (2013), The World Wide Web Consortium (W3C). https://www.w3.org/2001/sw/

      [11] Wikipedia, Semantic core https://en.wikipedia.org/wiki/Semantic_Channel

      [12] Landauer T, Foltz PW & Laham D (1998), An Introduction to Latent Semantic Analysis. Discourse Processes, 25(2-3), 259-284.

      [13] Mikolov T, Le QV, Sutskever I (2013), Exploiting Similarities among Languages for Machine Translation. https://arxiv.org/abs/1309.4168

      [14] Morozova YuI (2012), The construction of semantic vector spaces of different subject domains. The Third School of Young Scientists of the IPI RAS. Collection of reports. Moscow, 4-11.

      [15] Levy O, Golberg Y & Dagan I, Improving Distributional Similarity with Lessons Learned from Word Embeddings. Transactions of the Association for Computational Linguistics 3, 211–225.

      [16] Altszyler E, Sigman M, Ribeiro S & Slezak DF (2017), Comparative study of LSA vs Word2vec embeddings in small corpora: a case study in dreams database. Consciousness and Cognition 56, 178-187.

      [17] Serban IV, Lowe R, Henderson P, Charlin L & Pineau J (2018), A Survey of Available Corpora for Building Data-Driven Dialogue Systems. Dialogue and Discourse 9(1), 1-49.

      [18] Doan S, Vo BKH & Collier N (2011), An analysis of Twitter messages in the 2011 Tohoku Earthquake. International Conference on Electronic Healthcare, 58–66

      [19] Rennie JDM, Shih L, Teevan J & Karger DR (2003), Tackling the Poor Assumptions of Naive Bayes Text Classifiers. Proceedings of the Twentieth International Conference on Machine Learning (ICML). Washington, 616-623.

  • Downloads

  • How to Cite

    Evgenevich Sachkov, V., Aleksandrovich Akimov, D., & Aleksandrovich Pavelyev, S. (2018). The Use of Psycholinguistic Patterns in Interactive Systems of Active Information Retrieval. International Journal of Engineering & Technology, 7(4.38), 422-425. https://doi.org/10.14419/ijet.v7i4.38.24595