Discovering implicit intention-level knowledge from natural-language texts

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.