Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
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
INLG '00 Proceedings of the first international conference on Natural language generation - Volume 14
Generation of repeated references to discourse entities
ENLG '07 Proceedings of the Eleventh European Workshop on Natural Language Generation
Evaluating algorithms for the generation of referring expressions using a balanced corpus
ENLG '07 Proceedings of the Eleventh European Workshop on Natural Language Generation
Generation of referring expression with an individual imprint
ENLG '09 Proceedings of the 12th European Workshop on Natural Language Generation
The fingerprint of human referring expressions and their surface realization with graph transducers
INLG '08 Proceedings of the Fifth International Natural Language Generation Conference
Introducing shared tasks to NLG: the TUNA shared task evaluation challenges
Empirical methods in natural language generation
Generating referring expressions in context: the GREC task evaluation challenges
Empirical methods in natural language generation
Computational generation of referring expressions: A survey
Computational Linguistics
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Almost all natural language generation (NLG) systems are faced with the problem of the generation of referring expressions (GRE): given a symbol corresponding to an intended referent, how do we work out the semantic content of a referring expression that uniquely identifies the entity in question? This is now one of the most widely explored problems in NLG: over the last 15 years, a number of algorithms have been proposed for addressing different aspects of this problem, but the different approaches taken make it very difficult to compare and contrast the algorithms provided in any meaningful way. In this paper, we show how viewing the problem of referring expression generation as a search problem allows us to recast existing algorithms in a way that makes their similarities and differences clear.