Handling Sparse Data by Successive Abstraction
Abstract
A general, practical method for handling sparse data that avoids held-out data and iterative reestimation is derived from first principles. It has been tested on a part-of-speech tagging task and outperformed (deleted) interpolation with context-independent weights, even when the latter used a globally optimal parameter setting determined a posteriori.
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