Cooking up referring expressions
ACL '89 Proceedings of the 27th annual meeting on Association for Computational Linguistics
Generating minimal definite descriptions
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Generating referring expressions in open domains
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Evaluating algorithms for the generation of referring expressions using a balanced corpus
ENLG '07 Proceedings of the Eleventh European Workshop on Natural Language Generation
Building a semantically transparent corpus for the generation of referring expressions
INLG '06 Proceedings of the Fourth International Natural Language Generation Conference
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The semantic content determination or attribute selection of definite descriptions is one of the most traditional tasks in natural language generation. Algorithms of this kind are required to produce descriptions that are brief (or even minimal) and, at the same time, as close as possible to the choices made by human speakers. In this work we attempt to achieve a balance between brevity and humanlikeness by implementing a number of algorithms for the task. The algorithms are tested against descriptions produced by humans in two different domains, suggesting a strategy that is both computationally simple and comparable to the state of the art in the field.