Planning english referring expressions
Artificial Intelligence - Lecture notes in computer science 178
Generating descriptions that exploit a user's domain knowledge
Current research in natural language generation
Generating referring expressions: boolean extensions of the incremental algorithm
Computational Linguistics
Generating referring expressions in a domain of objects and processes (language representation)
Generating referring expressions in a domain of objects and processes (language representation)
Donnellan's distinction and a computational model of reference
ACL '86 Proceedings of the 24th annual meeting on Association for Computational Linguistics
Generating minimal definite descriptions
ACL '02 Proceedings of the 40th Annual Meeting on 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
Generating referring expressions in open domains
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
A system for generating descriptions of sets of objects in a rich variety
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Distinguishable entities: definition and properties
ENLG '09 Proceedings of the 12th European Workshop on Natural Language Generation
Generating references to parts of recursively structured objects
INLG '06 Proceedings of the Fourth International Natural Language Generation Conference
Computing intensional answers to questions - An inductive logic programming approach
Data & Knowledge Engineering
GRE3D7: a corpus of distinguishing descriptions for objects in visual scenes
UCNLG+EVAL '11 Proceedings of the UCNLG+Eval: Language Generation and Evaluation Workshop
| Hi-index | 0.00 |
Existing algorithms for generating referential descriptions to sets of objects have serious deficits: while incremental approaches may produce ambiguous and redundant expressions, exhaustive searches are computationally expensive. Mediating between these extreme control regimes, we propose a best-first searching algorithm for uniquely identifying sets of objects. We incorporate linguistically motivated preferences and several techniques to cut down the search space. Preliminary results show the effectiveness of the new algorithm.