Understanding Natural Language
Understanding Natural Language
Generating referring expressions: boolean extensions of the incremental algorithm
Computational Linguistics
Graph-based generation of referring expressions
Computational Linguistics
Planning natural language utterances to satisfy multiple goals
Planning natural language utterances to satisfy multiple goals
Generating referring expressions involving relations
EACL '91 Proceedings of the fifth conference on European chapter of the Association for Computational Linguistics
An algorithm for generating referential descriptions with flexible interfaces
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Cooking up referring expressions
ACL '89 Proceedings of the 27th annual meeting on Association for Computational Linguistics
A fast algorithm for the generation of referring expressions
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 1
Using aggregation for selecting content when generating referring expressions
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Generating minimal definite descriptions
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Logical form equivalence: the case of referring expressions generation
EWNLG '01 Proceedings of the 8th European workshop on Natural Language Generation - Volume 8
INLG '00 Proceedings of the first international conference on Natural language generation - Volume 14
Evaluating algorithms for the generation of referring expressions using a balanced corpus
ENLG '07 Proceedings of the Eleventh European Workshop on Natural Language Generation
Capturing acceptable variation in distinguishing descriptions
ENLG '07 Proceedings of the Eleventh European Workshop on Natural Language Generation
Distinguishable entities: definition and properties
ENLG '09 Proceedings of the 12th European Workshop on Natural Language Generation
Referring expressions as formulas of description logic
INLG '08 Proceedings of the Fifth International Natural Language Generation Conference
Attribute selection for referring expression generation: new algorithms and evaluation methods
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
Anchor-progression in spatially situated discourse: a production experiment
INLG '10 Proceedings of the 6th International Natural Language Generation Conference
Attribute-centric referring expression generation
Empirical methods in natural language generation
Introducing shared tasks to NLG: the TUNA shared task evaluation challenges
Empirical methods in natural language generation
Using logic in the generation of referring expressions
LACL'11 Proceedings of the 6th international conference on Logical aspects of computational linguistics
Computational generation of referring expressions: A survey
Computational Linguistics
The impact of visual context on the content of referring expressions
ENLG '11 Proceedings of the 13th European Workshop on Natural Language Generation
Offline sentence processing measures for testing readability with users
PITR '12 Proceedings of the First Workshop on Predicting and Improving Text Readability for target reader populations
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The natural language generation literature provides many algorithms for the generation of referring expressions. In this paper, we explore the question of whether these algorithms actually produce the kinds of expressions that people produce. We compare the output of three existing algorithms against a data set consisting of human-generated referring expressions, and identify a number of significant differences between what people do and what these algorithms do. On the basis of these observations, we suggest some ways forward that attempt to address these differences.