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Modeling Literary Style for Semi-automatic Generation of Poetry

  • Pablo Gervás
Conference paper
  • 697 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2109)

Abstract

The generation of formal poetry involves both complex creativity - usually exercised by a human poet - and strict algorithmic restrictions regarding the metrical structure of the poem - determined by literary tradition. Starting from a generating system that enforces automatically the metrical restrictions, this paper presents a model for the literary style of a user based on four key features for user preferences - word selection, language structures, poem planning, and restrictions on realisation - governing the generation of poetry from input data provided by the user - a prose paraphrase of the intended message, a task specific vocabulary, and a corpus of construction patterns. The system exploits the CBR paradigm as a means to evolve a case base (a vocabulary / construction pattern grouping) that effectively models the style of a specific user as a result of multiple iterations through the CBR cycle.

Keywords

natural language generation human-computer collaboration task modeling 

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References

  1. 1.
    Aamodt, A. & Plaza, E. (1994). Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. AI Communications, 7(i), pp 39–59.Google Scholar
  2. 2.
    Gervás, P., ‘WASP: Evaluation of Different Strategies for the Automatic Generation of Spanish Verse’, in: AISB-00 Symposium on Creative & Cultural Aspects and Applications of AI & Cognitive Science, 17th-18th April 2000, U. of Birmingham, England, pp 93–100.Google Scholar
  3. 3.
    Gervás, P., ‘An Expert System for the Composition of Formal Spanish Poetry’, in: Macintosh, A., Moulton, M., and Coenen, F. (eds.), Applications and Innovations in Intelligent Systems VIII, Springer Verlag, London Berlin Heidelberg, 2001, pp 19–34.Google Scholar
  4. 4.
    Horacek, H. and Busemann, S., ‘Towards a Methodology for Developing Application-Oriented Report Generation’, in: Günter, A. and Herzog, O. (eds.), 22nd German Conference on Artificial Intelligence (KI-98), Proceedings, Bremen, Germany, 1998.Google Scholar
  5. 5.
    Manurung, H.M., Ritchie, G., and Thompson, H., ‘’Towards a computational model of poetry generation’, in: AISB-00 Symposium on Creative & Cultural Aspects and Applications of AI & Cognitive Science, 17th-18th April 2000, U. of Birmingham, England.Google Scholar
  6. 6.
    Manurung, H.M., Ritchie, G., and Thompson, H., ‘A Flexible Integrated Architecture for Generating Poetic Texts’, Informatics Research Report, EDI-INF-RR-0016, Division of Informatics, U. of Edinburgh, May 2000.Google Scholar
  7. 7.
    Nederhof, M.-J., ‘Efficient generation of random sentences’, Encyclopaedia of Computer Science and Technology, Vol.41, Marcel Dekker, 1999, pp 45–65.Google Scholar
  8. 8.
    Stratil, M., and Oakley, R.J., ‘A Disputed Authorship Study of Two Plays Attributed to Tirso de Molina’, Literary and Linguistic Computing, Vol. 2,No. 3, 1987, pp 153–160.CrossRefGoogle Scholar
  9. 9.
    Tankard, J., ‘The Literary Detective’, Byte, February 1986, pp 231–238.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Pablo Gervás
    • 1
  1. 1.Departamento de Sistemas Informáticos y ProgramaciónUniversidad Complutense de MadridMadridSpain

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