Building natural language generation systems
Building natural language generation systems
Graph-based generation of referring expressions
Computational Linguistics
Generating Referring Expressions that Involve Gradable Properties
Computational Linguistics
Artificial Intelligence - Special volume on connecting language to the world
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Attribute selection for referring expression generation: new algorithms and evaluation methods
INLG '08 Proceedings of the Fifth International Natural Language Generation Conference
Natural reference to objects in a visual domain
INLG '10 Proceedings of the 6th International Natural Language Generation Conference
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This paper offers a solution to a small problem within a much larger problem. We focus on modelling how people use size in reference, words like "big" and "tall", which is one piece within the much larger problem of how people refer to visible objects. Examining size in isolation allows us to begin untangling a few of the complex and interacting features that affect reference, and we isolate a set of features that may be used in a hand-coded algorithm or a machine learning approach to generate one of six basic size types. The hand-coded algorithm generates a modifier type with a high correspondence to those observed in human data, and achieves 81.3% accuracy in an entirely new domain. This trails oracle accuracy for this task by just 8%. Features used by the hand-coded algorithm are added to a larger set of features in the machine learning approach, and we do not find a statistically significant difference between the precision and recall of the two systems. The input and output of these systems are a novel characterization of the factors that affect referring expression generation, and the methods described here may serve as one building block in future work connecting vision to language.