Skip to main content

Trusting Politicians’ Words (for Persuasive NLP)

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4919))

Abstract

This paper presents resources and lexical strategies for persuasive natural language processing. After the introduction of a specifically tagged corpus of political speeches, some forms of affective language processing in persuasive communication and prospects for application scenarios are provided. In particular Valentino, a prototype for valence shifting of existing texts, is described.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Perelman, C., Olbrechts-Tyteca, L.: The new Rhetoric: a treatise on Argumentation. Notre Dame Press (1969)

    Google Scholar 

  2. Reed, C., Grasso, F.: Recent advances in computational models of argument. International Journal of Intelligent Systems 22(1), 1–15 (2007)

    Article  MATH  Google Scholar 

  3. Miceli, M., deRosis, F., Poggi, I.: Emotional and non-emotional persuasion. Applied Artificial Intelligence 20 (2006)

    Google Scholar 

  4. Reed, C., Rowe, G.: Araucaria: Software for argument analysis, diagramming and representation. International Journal of AI Tools 14 (3-4), 961–980 (2004)

    Article  Google Scholar 

  5. Reiter, E., Robertson, R., Osman, L.: Lesson from a failure: Generating tailored smoking cessation letters. Artificial Intelligence 144, 41–58 (2003)

    Article  Google Scholar 

  6. Reiter, E., Sripada, S., Robertson, R.: Acquiring correct knowledge for natural language generation. Journal of Artificial Intelligence Research 18, 491–516 (2003)

    MATH  Google Scholar 

  7. Guerini, M., Stock, O., Zancanaro, M.: A taxonomy of strategies for multimodal persuasive message generation. Applied Artificial Intelligence Journal 21(2), 99–136 (2007)

    Article  Google Scholar 

  8. Toulmin, S.: The Use of Arguments. Cambridge University Press, Cambridge (1958)

    Google Scholar 

  9. Walton, D.: Argumentation Schemes for Presumptive Reasoning. Lawrence Erlbaum Associates, Mahwah (1996)

    Google Scholar 

  10. Zukerman, I., McConachy, R., Korb, K.: Using argumentation strategies in automated argument generation. In: Proceedings of the 1st International Natural Language Generation Conference, pp. 55–62 (2000)

    Google Scholar 

  11. Reed, C., Long, D.: Ordering and focusing in an architecture for persuasive discourse planning. In: Proceedings of the 6th European Workshop on Natural Language Generation (EWNLG 1997), Duisburg, Germany (1997)

    Google Scholar 

  12. Mazzotta, I.: deRosis, F., Carofiglio, V.: Portia: A user-adapted persuasion system in the healthy eating domain. IEEE Intelligent Systems, Special Issue on Argumentation Technology (in press, 2007)

    Google Scholar 

  13. Restificar, A.C., Ali, S.S., McRoy, S.W.: Arguer: Using argument schemas for argument detection and rebuttal in dialogs. In: Proceedings of the Seventh International Conference on User Modelling (UM-1999), June 20-24, 1999, Banff, Canada (1999)

    Google Scholar 

  14. Piwek, P.: An annotated bibliography of affective natural language generation. ITRI ITRI-02-02, University of Brighton (2002)

    Google Scholar 

  15. deRosis, F., Grasso, F.: Affective natural language generation. In: Paiva, A. (ed.) IWAI 1999. LNCS, vol. 1814, Springer, Heidelberg (2000)

    Google Scholar 

  16. Carofiglio, V., deRosis, F.: Combining logical with emotional reasoning in natural argumentation. In: Brusilovsky, P., Corbett, A.T., de Rosis, F. (eds.) UM 2003. LNCS, vol. 2702, Springer, Heidelberg (2003)

    Google Scholar 

  17. Carenini, G., Ng, R., Zwart, E.: Extracting knowledge from evaluative text. In: Proceedings of the 3rd international conference on Knowledge Capture, pp. 11–18 (2005)

    Google Scholar 

  18. Wilson, T., Wiebe, J., Hwa, R.: Just how mad are you? finding strong and weak opinion clauses. In: Proceedings of AAAI, pp. 761–769 (2004)

    Google Scholar 

  19. Carenini, G., Ng, R., Pauls, A.: Multi-document summarization of evaluative text. In: Proceedings of EACL (2006)

    Google Scholar 

  20. Radev, D., McKeown, K.: Building a generation knowledge source using internet-accessible newswire. In: Proceedings of the 5th Conference on Applied Natural Language Processing (1997)

    Google Scholar 

  21. Bull, P., Noordhuizen, M.: The mistiming of applause in political speeches. Journal of Language and Social Psychology 19, 275–294 (2000)

    Article  Google Scholar 

  22. Esuli, A., Sebastiani, F.: SentiWordNet: A publicly available lexical resource for opinion mining. In: Proceedings of the 5th Conference on Language Resources and Evaluation, Genova, IT, pp. 417–422 (2006)

    Google Scholar 

  23. Jing, H.: Usage of wordnet in natural language generation. In: Harabagiu, S. (ed.) Proceedings of the conference Use of WordNet in Natural Language Processing Systems, Somerset, New Jersey, Association for Computational Linguistics, pp. 128–134 (1998)

    Google Scholar 

  24. Pianta, E., Zanoli, R.: Tagpro: a system for italian pos tagging based on svm. Intelligenza Artificiale, Numero Speciale Strumenti di Elaborazione del Linguaggio Naturale per l’Italiano 4(2), 8–9 (2007)

    Google Scholar 

  25. Zanoli, R., Pianta, E.: Entitypro: exploiting svm for italian named entity recognition. Intelligenza Artificiale, Numero Speciale Strumenti di Elaborazione del Linguaggio Naturale per l’Italiano 4(2), 69–70 (2007)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Alexander Gelbukh

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Guerini, M., Strapparava, C., Stock, O. (2008). Trusting Politicians’ Words (for Persuasive NLP). In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2008. Lecture Notes in Computer Science, vol 4919. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78135-6_22

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-78135-6_22

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-78134-9

  • Online ISBN: 978-3-540-78135-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics