C4.5: programs for machine learning
C4.5: programs for machine learning
Generating referring expressions: boolean extensions of the incremental algorithm
Computational Linguistics
Graph-based generation of referring expressions
Computational Linguistics
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 that Involve Gradable Properties
Computational Linguistics
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
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
Conceptual coherence in the generation of referring expressions
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Algorithms for generating referring expressions: do they do what people do?
INLG '06 Proceedings of the Fourth 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
The GREC challenge: overview and evaluation results
INLG '08 Proceedings of the Fifth International Natural Language Generation Conference
The TUNA challenge 2008: overview and evaluation results
INLG '08 Proceedings of the Fifth International Natural Language Generation Conference
Modelling and evaluation of lexical and syntactic alignment with a priming-based microplanner
Empirical methods in natural language generation
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
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In this chapter, we take the view that much of the existing work on the generation of referring expressions has focused on aspects of the problem that appear to be somewhat artificial when we look more closely at human-produced referring expressions. In particular, we argue that an over-emphasis on the extent to which each property in a description performs a discriminatory function has blinded us to alternative approaches to referring expression generation that might be better-placed to provide an explanation of the variety we find in human-produced referring expressions. On the basis of an analysis of a collection of such data, we propose an alternative view of the process of referring expression generation which we believe is more intuitively plausible, is a better match for the observed data, and opens the door to more sophisticated algorithms that are freed of the constraints adopted in the literature so far.