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ECML
2005
Springer

Learning to Complete Sentences

9 years 5 months ago
Learning to Complete Sentences
Abstract. We consider the problem of predicting how a user will continue a given initial text fragment. Intuitively, our goal is to develop a “tab-complete” function for natural language, based on a model that is learned from text data. We consider two learning mechanisms that generate predictive models from collections of application-specific document collections: we develop an N-gram based completion method and discuss the application of instance-based learning. After developing evaluation metrics for this task, we empirically compare the model-based to the instance-based method and assess the predictability of call-center emails, personal emails, and weather reports.
Steffen Bickel, Peter Haider, Tobias Scheffer
Added 27 Jun 2010
Updated 27 Jun 2010
Type Conference
Year 2005
Where ECML
Authors Steffen Bickel, Peter Haider, Tobias Scheffer
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