NO- p are scores for running just the word segmentation model with no /t/-deletion rule on the data that includes /t/-deletion, NO-VAR for running just the word segmentation model on the data with no /t/-deletions.

To give an impression of the impact of /t/-deletion, we also report numbers for running only the segmentation model on the Buckeye data with no deleted /t/s and on the data with deleted /t/s.

Also note that in the GOLD- p condition, our joint Bigram model performs almost as well on data with /t/-deletions as the word segmentation model on data that includes no variation at all.

Bayesian word segmentation models try to compactly represent the observed data in terms of a small set of units (word types) and a short analysis (a small number of word tokens).

(2009) segmentation models , exact inference is infeasible for our joint model.

Although natural annotations in web text do not directly support the discriminative training of segmentation models , they do get rid of the implausible candidates for predictions of related characters.

We choose the perceptron algorithm (Collins, 2002) to train the classifier for the character classification-based word segmentation model .

Figure 2: Shrink of searching space for the character classification-based word segmentation model .

Table 1: Feature templates and instances for character classification-based word segmentation model .