Creating Robust Supervised Classifiers via Web-Scale N-gram Data
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@MISC{Bergsma_creatingrobust,
author = {Shane Bergsma and Emily Pitler and Dekang Lin},
title = {Creating Robust Supervised Classifiers via Web-Scale N-gram Data},
year = {}
}
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Abstract
In this paper, we systematically assess the value of using web-scale N-gram data in state-of-the-art supervised NLP classifiers. We compare classifiers that include or exclude features for the counts of various N-grams, where the counts are obtained from a web-scale auxiliary corpus. We show that including N-gram count features can advance the state-of-the-art accuracy on standard data sets for adjective ordering, spelling correction, noun compound bracketing, and verb part-of-speech disambiguation. More importantly, when operating on new domains, or when labeled training data is not plentiful, we show that using web-scale N-gram features is essential for achieving robust performance.
