Abstract
Georeferenced data sets are often large and complex. Natural language generation (NLG) systems are beginning to emerge that generate texts from such data. One of the challenges these systems face is the generation of geographic descriptions that refer to the location of events or patterns in the data. Based on our studies in the domain of meteorology we present an approach to generating approximate geographic descriptions involving regions, which incorporates domain knowledge and task constraints to model the utility of a description. Our evaluations show that NLG systems, because they can analyse input data exhaustively, can produce more fine-grained geographic descriptions that are potentially more useful to end users than those generated by human experts.
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Turner, R., Sripada, S., Reiter, E. (2010). Generating Approximate Geographic Descriptions. In: Krahmer, E., Theune, M. (eds) Empirical Methods in Natural Language Generation. EACL ENLG 2009 2009. Lecture Notes in Computer Science(), vol 5790. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15573-4_7
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DOI: https://doi.org/10.1007/978-3-642-15573-4_7
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