A Classification-driven Approach to Document Planning
Rafael Alves Paes de Oliveira, Eder Miranda de Novais, Roberto Paulo Andrioli de Araujo, Ivandré Paraboni · 2009
Document Planning - the task of deciding which content messages should be realised in a target document based on raw data provided by an underlying application, and how these messages should be structured - is arguably one of the most crucial tasks in Natural Language Generation (NLG). In this work we present a machine learning approach to Document Planning that is entirely trainable from annotated corpora, and which paves the way to our long-term goal of developing a text generator system based on a series of classifiers for a simple NLG application in the education domain.