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Verbalization of 3D Scenes Based on Natural Language Generation Techniques

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Part of the Studies in Computational Intelligence book series (SCI, volume 374)

Abstract

Basic ideas and requirements can be outlined by a design through ambiguous terms in order to define a desirable scene. Declarative modeling approach receives a rudimentary description and produces a set of scenes that are close to designer view. The reverse declarative modeling paradigm supports the designer to distinguish a set of scenes, accommodate further the pre-selected scenes to his needs, and produces a new enriched declarative description which initiates a new forward declarative design cycle for new promising scenes. The aim of the present work is to enhance the communication between the designer and machine, in such a way to increase the designer understanding and perception, by structuring a description in textual mode, reflecting all necessary semantic and geometric information, whenever the designer alters the pre-selected scenes.

Keywords

Natural Language Generation Communication Scene Understanding Declarative Modeling Semantic Model Knowledge-Based Systems Reverse Engineering 

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References

  1. 1.
    Plemenos, D.: A contribution to study and development of scene modeling, generation and display techniques – the MultiFormes project, Professorial dissertation, Nantes, France (November 1991)Google Scholar
  2. 2.
    Plemenos, D.: Declarative modeling by hierarchical decomposition. In: The Actual State of the MultiFormes project, International Conference Graphic. Con. 1995, St. Petersburg, Russia (July 1995)Google Scholar
  3. 3.
    Golfinopoulos, V., Bardis, G., Makris, D., Miaoulis, G., Plemenos, D.: Multiple scene understanding for declarative scene modeling. In: 3IA 2007 International Conference on Computer Graphics and Artificial Intelligence, Athens, Greece, pp. 39–49 (2007); ISBN 0-7695-3015-XGoogle Scholar
  4. 4.
    Russell, S., Norving, P.: Artificial Intelligence: A modern approach. Prentice Hall, New Jersey (2003)Google Scholar
  5. 5.
    Miaoulis, G.: Contribution à l’étude des Systèmes d’Information Multimédia et Intelligent dédiés à la Conception Déclarative Assistée par l’Ordinateur Le projet MultiCAD, Ph.D. Thesis, University of Limoges, France (2002)Google Scholar
  6. 6.
    Plemenos, D., Tamine, K.: Increasing the efficiency of declarative modeling. Constraint evaluation for the hierarchical decomposition approach. In: International Conference WSCG 1997, Plzen, Czech Republic (1997)Google Scholar
  7. 7.
    Makris, D.: Aesthetic–Aided Intelligent 3D Scene Synthesis. In: Miaoulis, G., Plemenos, D. (eds.) Intelligent Scene Modelling Information Systems. SCI, vol. 181. Springer, Heidelberg (2009) ISBN 978-3-540-92901-7CrossRefGoogle Scholar
  8. 8.
    Ravani, I., Makris, D., Miaoulis, G., Constantinides, P., Petridis, A., Plemenos, D.: Implementation of architecture-oriented knowledge framework in MultiCAD declarative scene modeling system. In: 1st Balcan Conference in Informatics, Greece (2003)Google Scholar
  9. 9.
    Dragonas, J.: Modélisation déclarative collaborative. Systèmes collaboratifs pour la modélisation déclarative en synthèse d’image, Ph.D. Thesis, University of Limoges, France (June 2006)Google Scholar
  10. 10.
    Bardis, G.: Intelligent Personalization in a Scene Modeling Environment. In: Miaoulis, G., Plemenos, D. (eds.) Intelligent Scene Modelling Information Systems. SCI, vol. 181. Springer, Heidelberg (2009) ISBN 978-3-540-92901-7CrossRefGoogle Scholar
  11. 11.
    Ravani, I., Makris, D., Miaoulis, G., Plemenos, D.: Concept-Based declarative description subsystem for CADD. In: 3IA 2004 International Conference, Limoges, France (2004)Google Scholar
  12. 12.
    Miaoulis, G., Plemenos, D., Skourlas, C.: MultiCAD Database: Toward a unified data and knowledge representation for database scene modeling. In: 3IA 2000 International Conference, Limoges, France (2000)Google Scholar
  13. 13.
    Golfinopoulos, V.: Understanding Scenes. In: Miaoulis, G., Plemenos, D. (eds.) Understanding Scenes. SCI, vol. 181. Springer, Heidelberg (2009); ISBN 978-3-540-92901-7Google Scholar
  14. 14.
    Sagerer, G., Niewmann, H.: Semantic networks for understanding scenes. Plenum Press, N. York (1997)Google Scholar
  15. 15.
    Reiter, E., Dale, R.: Building Natural Language Generation Systems. Cambridge University Press, Cambridge (2000)CrossRefGoogle Scholar
  16. 16.
    Mellish, C., Evans, R.: Implementation architectures for natural language generation. Natural Language Engineering 10(3/4), 261–282 (2004)CrossRefGoogle Scholar
  17. 17.
    Lu, W., Ng, H.T., Lee, W.S.: Natural language generation with tree conditional random fields. In: Conference on Empirical Methods in Natural Language Processing, pp. 400–409 (2009)Google Scholar
  18. 18.
    Garoufi, K., Koller, A.: Automated planning for situated language generation. In: 48th Annual Meeting of the Association for Computational Linguistics, Uppsala, Sweden, pp. 1573–1582 (2010)Google Scholar
  19. 19.
    Paris, C., Coloineau, N., Lampert, A., Vander Linden, K.: Discourse planning for information composition and delivery: a reusable platform. Natural Language Engineering 16(1), 61–98 (2009)CrossRefGoogle Scholar
  20. 20.
    Mellish, C., Scott, D., Cahill, L., Paiva, D., Evans, R., Reape, M.: A reference architecture for natural language generation systems. Natural Language Engineering 12(1), 1–34 (2006)CrossRefGoogle Scholar
  21. 21.
    O’Donnell, M., Mellish, C., Oberlander, J., Knott, A.: ILEX: an architecture for a dynamic hypertext generation system. Natural Language Engineering 7(13), 225–250 (2001)Google Scholar
  22. 22.
    Kosseim, L., Lapalme, G.: Choosing rhetorical structures to plan instructional texts. Computational Intelligence 16(3), 408–445 (2000)CrossRefGoogle Scholar
  23. 23.
    Davey, A.: Discourse Production: A Computer Model of Some Aspects of a Speaker. Edinburgh University Press, Edinburgh (1979)Google Scholar
  24. 24.
    Goldberg, E., Driedger, N., Kittredge, R.: Using natural-language processing to produce weather forecasts. IEEE Expert 9(2), 45–53 (1994)CrossRefGoogle Scholar
  25. 25.
    Lavoie, B., Rambow, O., Reiter, E.: A fast and portable realizer for text generation. In: Proceedings of the Fifth Conference on Applied Natural Language Processing, pp. 265–268 (1997)Google Scholar
  26. 26.
    Paris, C., Colineau, N., Lu, S., Vander Linden, K.: Automatically Generating Effective Online Help. International Journal on E-Learning 4(1), 83–103 (2005)Google Scholar
  27. 27.
    Ren, F., Du, Q.: Study on natutal language generation for spatial information representation. In: 5th International Conference on Fuzzy Systems and Knowledge Discovery, pp. 213–216 (2008)Google Scholar
  28. 28.
    Karberis, G., Kouroupetroglou, G.: Transforming Spontaneous Telegraphic Language to Well-Formed Greek Sentences for Alternative and Augmentative Communication. In: Vlahavas, I.P., Spyropoulos, C.D. (eds.) SETN 2002. LNCS (LNAI), vol. 2308, pp. 155–166. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  29. 29.
    Kojima, A., Tamura, T., Fukunaga, K.: Natural Language Description of Human Activities from Video Images Based on Concept Hierarchy of Actions. International Journal of Computer Vision 50(2), 171–184 (2002)zbMATHCrossRefGoogle Scholar
  30. 30.
    Sellinger, D.: Le modélisation géométrique déclarative interactive. Le couplage d’un modeleur déclaratif et d’un modeleur classique, Thèse, Université de Limoges, France (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Department of InformaticsTechnological Education Institution of AthensEgaleoGreece
  2. 2.XLIM LaboratoryUniversity of LimogesLimogesFrance

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