  Recently, string kernels have obtained state-of-the-art results in various
text classification tasks such as Arabic dialect identification or native
language identification. In this paper, we apply two simple yet effective
transductive learning approaches to further improve the results of string
kernels. The first approach is based on interpreting the pairwise string kernel
similarities between samples in the training set and samples in the test set as
features. Our second approach is a simple self-training method based on two
learning iterations. In the first iteration, a classifier is trained on the
training set and tested on the test set, as usual. In the second iteration, a
number of test samples (to which the classifier associated higher confidence
scores) are added to the training set for another round of training. However,
the ground-truth labels of the added test samples are not necessary. Instead,
we use the labels predicted by the classifier in the first training iteration.
By adapting string kernels to the test set, we report significantly better
accuracy rates in English polarity classification and Arabic dialect
identification.
