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A Classification-driven Approach to Document Planning

By Rafael L. De Oliveira, Eder M. De Novais, Roberto P. A. De Araujo and Ré Paraboni


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

Topics: Content Selection
Year: 2014
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