Unfortunately , as shown in ( Okanohara and Tsujii , 2007 ) , with the represetation of sentences that we use , linear classifiers can not discriminate real sentences from sentences sampled from a trigram , which is the model we use as a baseline , so here we resort to a non-linear large-margin classifier ( see section 3 for details ) .
a discriminative language model with pseudonegative samples
Daisuke Okanohara Jun'ichi Tsujii
As these are computationally very efficient , the authors could use a variant of Gibbs sampling for generating negative samples . Unfortunately , as shown in ( Okanohara and Tsujii , 2007 ) , with the represetation of sentences that we use , linear classifiers can not discriminate real sentences from sentences sampled from a trigram , which is the model we use as a baseline , so here we resort to a non-linear large-margin classifier ( see section 3 for details ) . While large-margin classifiers consistently out-perform other learning algorithms in many NLP tasks , their non-linear variations are also notoriously slow when it comes to computing their decision function -- taking time that can be linear in the size of their training data .
Unfortunately , as shown in ( @@CITATION ) , with the represetation of sentences that we use , linear classifiers can not discriminate real sentences from sentences sampled from a trigram , which is the model we use as a baseline , so here we resort to a non-linear large-margin classifier ( see section 3 for details ) .