Skip to main content
Springer Nature Link
Log in

Visualizing and Processing Information Not Uttered in Spoken Political and Journalistic Data: From Graphical Representations to Knowledge Graphs in an Interactive Application

  • Conference paper
  • First Online:
Human-Computer Interaction. Technological Innovation (HCII 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13303))

Included in the following conference series:

  • 1602 Accesses

Abstract

The present interactive application targets to the processing and direct generation and use of information not uttered, converting it into “visible”, processable information in the form of knowledge graphs and, subsequently, training data, for neural networks and other uses. In-depth understanding and un-biased evaluation of interviews and discussions in spoken political and journalistic texts is targeted, especially when an international public is concerned. Special emphasis is placed on parameters concerning Chinese speakers within the international media and community.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Alexandris, C., Floros, V., Mourouzidis, D.: Graphic representations of spoken interactions from journalistic data: persuasion and negotiations. In: Kurosu, M. (ed.) HCII 2021. LNCS, vol. 12764, pp. 3–17. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-78468-3_1

    Chapter  Google Scholar 

  2. Alexandris, C.: Registering the impact of words in spoken political and journalistic texts. J. Hum. Lang. Rights Secur. 26–48 (2021). Peoples Friendship University (RUDN), Moscow, Russian Federation. https://doi.org/10.22363/2713-0614-2021-1-1-26-48

  3. Alexandris, C.: Issues in Multilingual Information Processing of Spoken Political and Journalistic Texts in the Media and Broadcast News, Cambridge Scholars, Newcastle upon Tyne, UK (2020)

    Google Scholar 

  4. Alexandris, C., Mourouzidis, D., Floros, V.: Generating graphic representations of spoken interactions revisited: the tension factor and information not uttered in journalistic data. In: Kurosu, M. (ed.) HCII 2020. LNCS, vol. 12181, pp. 523–537. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-49059-1_39

    Chapter  Google Scholar 

  5. Alexandris, C.: Evaluating cognitive bias in two-party and multi-party spoken interactions. In: Proceedings of Interpretable AI for Well-Being: Understanding Cognitive Bias and Social Embeddedness (IAW 2019) in Conjunction with AAAI Spring Symposium (SS-19-03), Stanford University, Palo Alto, CA. http://ceur-ws.org/Vol-2448

  6. Alexandris, C.: Visualizing pragmatic features in spoken interaction: intentions, behavior and evaluation. In: Proceedings of the 1st International Conference on Linguistics Research on the Era of Artificial Intelligence – LREAI, Dalian, 25–27 October 2019. Dalian Maritime University (2019)

    Google Scholar 

  7. Alexandris, C.: Measuring cognitive bias in Spoken interaction and conversation: generating visual representations. In: Beyond Machine Intelligence: Understanding Cognitive Bias and Humanity for Well-Being AI Papers from the AAAI Spring Symposium, Stanford University, Technical report SS-18-03, pp. 204–206. AAAI Press, Palo Alto (2018)

    Google Scholar 

  8. Alexandris, C.: English, German and the international “semi-professional” translator: a morphological approach to implied connotative features. J. Lang. Transl. 11(2), 7–46 (2010). Sejong University, Korea

    Google Scholar 

  9. Arockiaraj, C.M.: Applications of neural networks in data mining. Int. J. Eng. Sci. 3(1), 8–11 (2013)

    Google Scholar 

  10. Austin, J.L.: How to Do Things with Words, 2nd edn. University Press, Oxford Paperbacks, Oxford (1976). Urmson, J.O., Sbisà, M. (eds.) (1962)

    Google Scholar 

  11. Carlson, L., Marcu, D., Okurowski, M.E.: Building a discourse-tagged corpus in the framework of rhetorical structure theory. In: Proceedings of the 2nd SIGDIAL Workshop on Discourse and Dialogue, Eurospeech 2001, Denmark, September 2001

    Google Scholar 

  12. Du, J., Alexandris, C., Mourouzidis, D., Floros, V., Iliakis, A.: Controlling interaction in multilingual conversation revisited: a perspective for services and interviews in Mandarin Chinese. In: Kurosu, M. (ed.) HCI 2017. LNCS, vol. 10271, pp. 573–583. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-58071-5_43

    Chapter  Google Scholar 

  13. Evans, N.J., Park, D.: Rethinking the persuasion knowledge model: schematic antecedents and associative outcomes of persuasion knowledge activation for covert advertising. J. Curr. Issues Res. Advert. 36(2), 157–176 (2015). https://doi.org/10.1080/10641734.2015.1023873

    Article  Google Scholar 

  14. Grice, H.P.: Studies in the Way of Words. Harvard University Press, Cambridge (1989)

    Google Scholar 

  15. Grice, H.P.: Logic and conversation. In: Cole, P., Morgan, J. (eds.) Syntax and Semantics, vol. 3. Academic Press, New York (1975)

    Google Scholar 

  16. Hatim, B.: Communication Across Cultures: Translation Theory and Contrastive Text Linguistics. University of Exeter Press, Exeter (1997)

    Google Scholar 

  17. Hedderich, M.A., Klakow, D.: Training a neural network in a low-resource setting on automatically annotated noisy data. In: Proceedings of the Workshop on Deep Learning Approaches for Low-Resource NLP, Melbourne, Australia, pp. 12–18. Association for Computational Linguistics-ACL (2018)

    Google Scholar 

  18. Hilbert, M.: Toward a synthesis of cognitive biases: how noisy information processing can bias human decision making. Psychol. Bull. 138(2), 211–237 (2012)

    Article  MathSciNet  Google Scholar 

  19. Liu, B.: Sentiment Analysis and Opinion Mining. Morgan & Claypool, San Rafael (2012)

    Google Scholar 

  20. Ma, J.: A comparative analysis of the ambiguity resolution of two English-Chinese MT approaches: RBMT and SMT. Dalian Univ. Technol. J. 31(3), 114–119 (2010)

    Google Scholar 

  21. Marcu, D.: Discourse trees are good indicators of importance in text. In: Mani, I., Maybury, M. (eds.) Advances in Automatic Text Summarization, pp. 123–136. The MIT Press, Cambridge (1999)

    Google Scholar 

  22. Mittal, S., Joshi, A., Finin, T.: Thinking, Fast and Slow: Combining Vector Spaces and Knowledge Graphs. arXiv:1708.03310v2 [cs.AI] (2017)

  23. Mountantonakis, M., Tzitzikas, Y.: Knowledge graph embeddings over hundreds of linked datasets. In: Garoufallou, E., Fallucchi, F., William De Luca, E. (eds.) MTSR 2019. CCIS, vol. 1057, pp. 150–162. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-36599-8_13

    Chapter  Google Scholar 

  24. Mourouzidis, D., Floros, V., Alexandris, C.: Generating graphic representations of spoken interactions from journalistic data. In: Kurosu, M. (ed.) HCII 2019. LNCS, vol. 11566, pp. 559–570. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-22646-6_42

    Chapter  Google Scholar 

  25. Nass, C., Brave, S.: Wired for Speech: How Voice Activates and Advances the Human-Computer Relationship. The ΜΙΤ Press, Cambridge (2005)

    Google Scholar 

  26. Paltridge, B.: Discourse Analysis: An Introduction. Bloomsbury Publishing, London (2012)

    Book  Google Scholar 

  27. Pan, Y.: Politeness in Chinese face-to-face interaction. In: Advances in Discourse Processes Series, vol. 67. Ablex Publishing Corporation, Stamford (2000)

    Google Scholar 

  28. Plutchik, R.: A psychoevolutionary theory of emotions. Soc. Sci. Inf. 21, 529–553 (1982). https://doi.org/10.1177/053901882021004003

    Article  Google Scholar 

  29. Poria, S., Cambria, E., Hazarika, D., Mazumder, N., Zadeh, A., Morency, L.-P.: Context-dependent sentiment analysis in user-generated videos. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, Vancouver, Canada, 30 July–4 August 2017, pp. 873–888. Association for Computational Linguistics – ACL (2017). https://doi.org/10.18653/v1/P17-1081

  30. Rocklage, M.D., Rucker, D.D., Nordgren, L.F.: Psychol. Sci. 29(5), 749–760 (2018). https://doi.org/10.1177/0956797617744797

  31. Sacks, H., Schegloff, E.A., Jefferson, G.: A simplest systematics for the organization of turn-taking for conversation. Language 50, 696–735 (1974)

    Article  Google Scholar 

  32. Searle, J.R.: Speech Acts: An Essay in the Philosophy of Language. Cambridge University Press, Cambridge (1969)

    Book  Google Scholar 

  33. Shah, K., Kopru, S., Ruvini, J.-D.: Neural network based extreme classification and similarity models for product matching. In: Proceedings of NAACL-HLT 2018, New Orleans, Louisiana, 1–6 June 2018, pp. 8–15. Association for Computational Linguistics-ACL (2018)

    Google Scholar 

  34. Skonk, K.: 5 Types of Negotiation Skills, Program on Negotiation Daily Blog. Harvard Law School, 14th May 2020. https://www.pon.harvard.edu/daily/negotiation-skills-daily/types-of-negotiation-skills/. Accessed 11 Nov 2020

  35. Stede, M., Taboada, M., Das, D.: Annotation Guidelines for Rhetorical Structure. Manuscript. University of Potsdam and Simon Fraser University, March 2017

    Google Scholar 

  36. Tran, H.N., Takashu, A.: Analyzing knowledge graph embedding methods from a multi-embedding interaction perspective. In: Proceedings of the 1st International Workshop on Data Science for Industry 4.0 (DSI4) at EDBT/ICDT 2019 Joint Conference (2019). https://arxiv.org/abs/1903.11406

  37. Trofimova, I.: Observer bias: an interaction of temperament traits with biases in the semantic perception of lexical material. PloS ONE 9(1), e85677 (2014)

    Google Scholar 

  38. Wang, M., Qiu, L.: A survey on knowledge graph embeddings for link prediction. Symmetry 13, 485 (2021). https://doi.org/10.3390/sym13030485

    Article  Google Scholar 

  39. Wardhaugh, R.: An Introduction to Sociolinguistics, 2nd edn. Blackwell, Oxford (1992)

    Google Scholar 

  40. Williams, J.D., Asadi, K., Zweig, G.: Hybrid code networks: practical and efficient end-to-end dialog control with supervised and reinforcement learning. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, Vancouver, Canada, 30 July–4 August 2017, pp. 665–677. Association for Computational Linguistics (ACL) (2017)

    Google Scholar 

  41. Wilson, M., Wilson, T.P.: An oscillator model of the timing of turn taking. Psychonomic Bull. Rev. 12(6), 957–968 (2005)

    Article  Google Scholar 

  42. Yu, Z., Yu, Z., Aoyama, H., Ozeki, M., Nakamura, Y.: Capture, recognition, and visualization of human semantic interactions in meetings. In: Proceedings of PerCom, Mannheim, Germany (2010)

    Google Scholar 

  43. Zeldes, A.: rstWeb - a browser-based annotation interface for rhetorical structure theory and discourse relations. In: Proceedings of NAACL-HLT 2016 System Demonstrations, San Diego, CA, pp. 1–5 (2016). http://aclweb.org/anthology/N/N16/N16-3001.pdf

Download references

Author information

Authors and Affiliations

  1. National and Kapodistrian University of Athens, Athens, Greece

    Christina Alexandris & Vasilios Floros

  2. Lab of Language Engineering and Computing, Faculty of Chinese Language and Culture, Guangdong University of Foreign Studies, Guangzhou, China

    Jiali Du

  3. European Communication Institute (ECI), Danube University Krems and National Technical University of Athens, Athens, Greece

    Christina Alexandris & Vasilios Floros

Authors
  1. Christina Alexandris
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jiali Du
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Vasilios Floros
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Christina Alexandris .

Editor information

Editors and Affiliations

  1. The Open University of Japan, Chiba, Japan

    Masaaki Kurosu

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Alexandris, C., Du, J., Floros, V. (2022). Visualizing and Processing Information Not Uttered in Spoken Political and Journalistic Data: From Graphical Representations to Knowledge Graphs in an Interactive Application. In: Kurosu, M. (eds) Human-Computer Interaction. Technological Innovation. HCII 2022. Lecture Notes in Computer Science, vol 13303. Springer, Cham. https://doi.org/10.1007/978-3-031-05409-9_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-05409-9_16

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-05408-2

  • Online ISBN: 978-3-031-05409-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics