Improved Modeling of Out-Of-Vocabulary Words Using Morphological Classes
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by
Thomas Müller
,
Hinrich Schütze
BibTeX
@MISC{Müller_improvedmodeling,
author = {Thomas Müller and Hinrich Schütze},
title = {Improved Modeling of Out-Of-Vocabulary Words Using Morphological Classes},
year = {}
}
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Abstract
We present a class-based language model that clusters rare words of similar morphology together. The model improves the prediction of words after histories containing outof-vocabulary words. The morphological features used are obtained without the use of labeled data. The perplexity improvement compared to a state of the art Kneser-Ney model is 4 % overall and 81 % on unknown histories. 1
