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Springer, Berlin, Heidelberg

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10.1016/0144-4565(89)90028-0

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Learning to Complete Sentences

Authors: Steffen Bickel, Peter Haider, Tobias Scheffer,

Publish Date: 2005/10/3
Volume: , Issue:, Pages: 497-504
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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.


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