Generating approximate geographic descriptions

Authors:
Ross Turner;Somayajulu Sripada;Ehud Reiter
Affiliations:
Nokia Gate5 GmbH, Berlin, Germany;Department of Computing Science, University of Aberdeen, UK;Department of Computing Science, University of Aberdeen, UK
Venue:
Empirical methods in natural language generation
Year:
2010

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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.