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Erschienen in:
2005 | OriginalPaper | Buchkapitel
Learning to Complete Sentences
verfasst von : Steffen Bickel, Peter Haider, Tobias Scheffer
Erschienen in: Machine Learning: ECML 2005
Verlag: Springer Berlin Heidelberg
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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.