Generating coordinated natural language and 3D animations for complex spatial explanations
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Spatial Cognition and Computation
Choosing words in computer-generated weather forecasts
Artificial Intelligence - Special volume on connecting language to the world
Incremental generation of spatial referring expressions in situated dialog
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Generating spatio-temporal descriptions in pollen forecasts
EACL '06 Proceedings of the Eleventh Conference of the European Chapter of the Association for Computational Linguistics: Posters & Demonstrations
Using spatial reference frames to generate grounded textual summaries of georeferenced data
INLG '08 Proceedings of the Fifth International Natural Language Generation Conference
The use of spatial relations in referring expression generation
INLG '08 Proceedings of the Fifth International Natural Language Generation Conference
What's in a message?: interpreting geo-referenced data for the visually-impaired
INLG '08 Proceedings of the Fifth International Natural Language Generation Conference
A simple domain-independent probabilistic approach to generation
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Expert Systems with Applications: An International Journal
Computational generation of referring expressions: A survey
Computational Linguistics
Concept-to-text generation via discriminative reranking
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
A global model for concept-to-text generation
Journal of Artificial Intelligence Research
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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 referring to the location of events or patterns in the data. Based on our studies in the domain of meteorology we present a two staged approach to generating geographic descriptions. The first stage involves using domain knowledge based on the task context to select a frame of reference, and the second involves using constraints imposed by the end user to select values within a frame of reference. Because geographic concepts are inherently vague our approach does not guarantee a distinguishing description. Our evaluation studies show that NLG systems, because they can analyse input data exhaustively, can produce more fine-grained geographic descriptions that are more useful to end users than those generated by human experts.