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Linguistic Approaches to Robotics: From Text Analysis to the Synthesis of Behavior

  • Artemy KotovEmail author
  • Nikita Arinkin
  • Ludmila Zaidelman
  • Anna Zinina
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 943)

Abstract

We examine the problem of “understanding robots” and design an F–2 emotional robot to “understand” speech and to support human-like behavior. The suggested system is an applied implementation of the theoretical concept of robotic information flow, suggested by M. Minsky (“proto-specialists”) and A. Sloman (CogAff). This system works with real world input – natural texts, speech sound – and produces natural behavioral output – speech, gestures and facial expressions. Unlike other chatbots, the system relies on semantic representation and operates with a set of d-scripts (equivalents to proto-specialists), extracted from advertising and mass media texts as a classification of basic emotional patterns. The process of “understanding” is modelled as the selection of a relevant d-script for the incoming utterance.

Keywords

Syntactic parsers Semantic processing Robot companions Emotional agents 

Notes

Acknowledgements

Design of the syntactic parser was supported by RFBR grant 16-29-09601 ofi_m, development of negative emotional patterns was supported by RSF grant 17-78-30029, and design of the F-2 robot was supported by the NRC “Kurchatov Institute” (05.07.2018 № 1601).

References

  1. 1.
    Minsky, M.L.: The Society of Mind. Touchstone Book, New-York, London (1988)Google Scholar
  2. 2.
    Sloman, A.: Beyond shallow models of emotion. Cognit. Process. 2, 177–198 (2001)Google Scholar
  3. 3.
    Sloman, A., Chrisley, R.: Virtual machines and consciousness. J. Conscious. Stud. 10, 133–172 (2003)Google Scholar
  4. 4.
    Sloman, A.: Varieties of affect and the CogAff architecture schema. In: Johnson, C. (ed.) Proceedings Symposium on Emotion, Cognition, and Affective Computing, AISB’01 Convention, York, vol. 10, pp. 39–48 (2001)Google Scholar
  5. 5.
    Su, F., Markert, K.: From words to senses: a case study of subjectivity recognition. In: Proceedings of the 22nd International Conference on Computational Linguistics, vol. 1, pp. 825–832. Association for Computational Linguistics, Manchester (2008)Google Scholar
  6. 6.
    Chetviorkin, I.I.: Testing the sentiment classification approach in various domains – ROMIP 2011. Comput. Linguist. Intellect. Technol. 2(11), 15–26 (2012)Google Scholar
  7. 7.
    Poroshin, V.: Proof of concept statistical sentiment classification at ROMIP 2011. Comput. Linguist. Intellect. Technol. 2(11), 60–65 (2012)Google Scholar
  8. 8.
    Katz, B.: From sentence processing to information access on the world wide web. In: AAAI Spring Symposium on Natural Language Processing for the World Wide Web (1997)Google Scholar
  9. 9.
    Mavljutov, R.R., Ostapuk, N.A.: Using basic syntactic relations for sentiment analysis. Comput. Linguist. Intellect. Technol. 2(12), 91–100 (2013)Google Scholar
  10. 10.
    Anisimovich, K.V., Druzhkin, K.J., Minlos, F.R., Petrova, M.A., Selegey, V.P., Zuev, K.A.: Syntactic and semantic parser based on ABBYY Compreno linguistic technologies. Comput. Linguist. Intellect. Technol. 2(11), 91–103 (2012)Google Scholar
  11. 11.
    Recupero, D.R., Presutti, V., Consoli, S., Gangemi, A., Nuzzolese, A.G.: Sentilo: frame-based sentiment analysis. Cognit. Comput. 7, 211–225 (2014)Google Scholar
  12. 12.
    Kotov, A., Zinina, A., Filatov, A.: Semantic parser for sentiment analysis and the emotional computer agents. In: Proceedings of the AINL-ISMW FRUCT 2015, pp. 167–170 (2015)Google Scholar
  13. 13.
    Kotov, A.A.: Mechanisms of speech influence in publicistic mass media texts. Ph.d. thesis. RSUH, Moscow (2003, in Russian)Google Scholar
  14. 14.
    Kotov, A.A.: Mechanisms of Speech Influence. Kurchatov Institute, Moscow (2017)Google Scholar
  15. 15.
    Kopp, S., et al.: Towards a common framework for multimodal generation: the behavior markup language. In: Gratch, J., Young, M., Aylett, R., Ballin, D., Olivier, P. (eds.) IVA 2006. LNCS (LNAI), vol. 4133, pp. 205–217. Springer, Heidelberg (2006).  https://doi.org/10.1007/11821830_17CrossRefGoogle Scholar
  16. 16.
    Vilhjálmsson, H., et al.: The behavior markup language: recent developments and challenges. In: Pelachaud, C., Martin, J.-C., André, E., Chollet, G., Karpouzis, K., Pelé, D. (eds.) IVA 2007. LNCS (LNAI), vol. 4722, pp. 99–111. Springer, Heidelberg (2007).  https://doi.org/10.1007/978-3-540-74997-4_10CrossRefGoogle Scholar
  17. 17.
    Kotov, A., Budyanskaya, E.: The Russian emotional corpus: communication in natural emotional situations. Comput. Linguist. Intellect. Technol. 11(18), 296–306 (2012)Google Scholar
  18. 18.
    Kotov, A.A., Zinina, A.A.: Functional analysis of nonverbal communicative behavior. Comput. Linguist. Intellect. Technol. 1(14), 299–310 (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.National Research Center “Kurchatov Institute”MoscowRussia
  2. 2.Russian State University for the HumanitiesMoscowRussia

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