3 research outputs found
Multi-D Kneser-Ney Smoothing Preserving the Original Marginal Distributions
Smoothing is an essential tool in many NLP tasks, therefore numerous
techniques have been developed for this purpose in the past. One of the most
widely used smoothing methods are the Kneser-Ney smoothing (KNS) and its
variants, including the Modified Kneser-Ney smoothing (MKNS), which are widely
considered to be among the best smoothing methods available. Although when
creating the original KNS the intention of the authors was to develop such a
smoothing method that preserves the marginal distributions of the original
model, this property was not maintained when developing the MKNS.
In this article I would like to overcome this and propose such a refined
version of the MKNS that preserves these marginal distributions while keeping
the advantages of both previous versions. Beside its advantageous properties,
this novel smoothing method is shown to achieve about the same results as the
MKNS in a standard language modelling task