Abstract
In this paper we propose a new approach to automatic discovery of implicit rhetorical information from texts based on evolutionary computation methods. In order to guide the search for rhetorical connections from natural-language texts, the model uses previously obtained training information which involves semantic and structural criteria. The main features of the model and new designed operators and evaluation functions are discussed, and the different experiments assessing the robustness and accuracy of the approach are described. Experimental results show the promise of evolutionary methods for rhetorical role discovery.
This research is partially sponsored by the National Council for Scientific and Technological Research (FONDECYT, Chile) under grant number 1070714 “{itAn Interactive Natural-Language Dialogue Model for Intelligent Filtering based on Patterns Discovered from Text Documents”
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Atkinson, J., Ferreira, A., Aravena, E. (2009). Discovering Implicit Intention-Level Knowledge from Natural-Language Texts. In: Bramer, M., Petridis, M., Coenen, F. (eds) Research and Development in Intelligent Systems XXV. SGAI 2008. Springer, London. https://doi.org/10.1007/978-1-84882-171-2_18
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DOI: https://doi.org/10.1007/978-1-84882-171-2_18
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