Abstract
Generated graphic representations for interactions involving persuasion and negotiations are intended to assist evaluation, training and decision-making processes and for the construction of respective models. As described in previous research, discourse and dialog structure are evaluated by the y level value around which the graphic representation is developed. Special emphasis is placed on emotion used as a tool for persuasion with the respective expressions, pragmatic elements and the depiction of information not uttered and their subsequent use in the collection of empirical and statistical data.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
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)
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
Alexandris, C.: Evaluating cognitive bias in two-party and multi-party spoken interactions. In: Proceedings from the AAAI Spring Symposium, Stanford University (2019)
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, October 25–27, 2019, Dalian Maritime University (2019)
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, CA (2018)
Alexandris, C., Nottas, M., Cambourakis, G.: Interactive evaluation of pragmatic features in spoken journalistic texts. In: Kurosu, M. (ed.) HCI 2015. LNCS, vol. 9171, pp. 259–268. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-21006-3_26
Alexandris, C.: English, german and the international “semi-professional” translator: a morphological approach to implied connotative features. J. Lang. Transl. Sejong Univ. Korea 11(2), 7–46 (2010)
Arockiaraj, C.M.: Applications of neural networks in data mining. Int. J. Eng. Sci. 3(1), 8–11 (2013)
Austin, J.L.: How to Do Things with Words, 2nd edn. University Press, Oxford Paperbacks, Oxford (1962).(Urmson, J.O., Sbisà, M. (eds.) 1976)
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 (2001)
Cutts, M.: Oxford Guide to Plain English, 4th edn. Oxford University Press, Oxford, UK (2013)
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
Floros, V., Mourouzidis, D.: Multiple Task Management in a Dialog System for Call Centers. Master’s thesis, Department of Informatics and Telecommunications, National University of Athens, Greece (2016)
Grice, H.P.: Studies in the Way of Words. Harvard University Press, Cambridge, MA (1989)
Grice, H.P.: Logic and conversation. In: Cole, P., Morgan, J. (eds.) Syntax and Semantics, vol. 3, pp. 41–58. Academic Press, New York (1975)
Hatim, B.: Communication Across Cultures: Translation Theory and Contrastive Text Linguistics. University of Exeter Press, Exeter, UK (1997)
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)
Hilbert, M.: Toward a synthesis of cognitive biases: how noisy information processing can bias human decision making. Psychol. Bull. 138(2), 211–237 (2012)
Lewis, J.R.: Introduction to Practical Speech User Interface Design for Interactive Voice Response Applications, IBM Software Group, USA, Tutorial T09 presented at HCI 2009 San Diego. CA, USA (2009)
Liu, B.: Sentiment Analysis and Opinion Mining. Morgan & Claypool, San Rafael, CA (2012)
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)
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, MA (1999)
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
Nass, C., Brave, S.: Wired for Speech: How Voice Activates and Advances the Human-Computer Relationship. The ΜΙΤ Press, Cambridge, MA (2005)
Nottas, M., Alexandris, C., Tsopanoglou, A., Bakamidis, S.: A hybrid approach to dialog input in the citzenshield dialog system for consumer complaints. In: Proceedings of HCI 2007, Bei**g, People’s Republic of China (2007)
Paltridge, B.: Discourse Analysis: An Introduction. Bloomsbury Publishing, London (2012)
Pan, Y.: Politeness in Chinese Face-to-Face Interaction. Advances in Discourse Processes series, vol. 67. Ablex Publishing Corporation, Stamford, CT, USA (2000)
Plutchik, R.: A psychoevolutionary theory of emotions. Soc. Sci. Inf. 21, 529–553 (1982). https://doi.org/10.1177/053901882021004003
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, July 30-August 4 2017, pp. 873–88. Association for Computational Linguistics - ACL (2017). https://doi.org/10.18653/v1/P17-1081
Rocklage, M., Rucker, D., Nordgren, L.: Persuasion, emotion, and language: the intent to persuade transforms language via emotionality. Psychol. Sci. 29(5), 749–760 (2018). https://doi.org/10.1177/0956797617744797
Sacks, H., Schegloff, E.A., Jefferson, G.: A simplest systematics for the organization of turn-taking for conversation. Language 50, 696–735 (1974)
Searle, J.R.: Speech Acts: An Essay in the Philosophy of Language. Cambridge University Press, Cambridge, MA (1969)
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, June 1-6, 2018, pp. 8–15. Association for Computational Linguistics-ACL (2018)
Skonk, K.: 5 Types of Negotiation Skills, Program on Negotiation Daily Blog, Harvard Law School, 14 May 2020. https://www.pon.harvard.edu/daily/negotiation-skills-daily/types-of-negotiation-skills/. Accessed 11 Nov 2020
Stede, M., Taboada, M., Das, D.: Annotation Guidelines for Rhetorical Structure Manuscript. University of Potsdam and Simon Fraser University, Potsdam (2017)
Trofimova, I.: Observer bias: an interaction of temperament traits with biases in the semantic perception of lexical material. PLoS ONE 9(1), e85677 (2014). https://doi.org/10.1371/journal.pone.0085677
Wardhaugh, R.: An Introduction to Sociolinguistics, 2nd edn. Blackwell, Oxford, UK (1992)
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, July 30-August 4 2017, pp. 665–677. Association for Computational Linguistics (ACL) (2017)
Wilson, M., Wilson, T.P.: An oscillator model of the timing of turn taking. Psychon. Bull. Rev. 12(6), 957–968 (2005)
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 (2010)
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
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Alexandris, C., Floros, V., Mourouzidis, D. (2021). Graphic Representations of Spoken Interactions from Journalistic Data: Persuasion and Negotiations. In: Kurosu, M. (eds) Human-Computer Interaction. Design and User Experience Case Studies. HCII 2021. Lecture Notes in Computer Science(), vol 12764. Springer, Cham. https://doi.org/10.1007/978-3-030-78468-3_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-78468-3_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-78467-6
Online ISBN: 978-3-030-78468-3
eBook Packages: Computer ScienceComputer Science (R0)