Abstract: For many text classification tasks, there is a major problem posed by the lack of labeled data in a target domain. Although classifiers for a target domain can be trained on labeled text data from a related source domain, the accuracy of such classifiers is usually lower in the cross-domain setting. Recently, string kernels have obtained state-of-the-art results in various text classification tasks such as native language identification or automatic essay scoring. Moreover, classifiers based on string kernels have been found to be robust to the distribution gap between different domains. In this paper, we formally describe an algorithm composed of two simple yet effective transductive learning approaches to further improve the results of string kernels in cross-domain settings. By adapting string kernels to the test set without using the ground-truth test labels, we report significantly better accuracy rates in cross-domain English polarity classification.

Introduction:  Domain shift is a fundamental problem in machine learning, that has attracted a lot of attention in the natural language processing and vision communities BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 . To understand and address this problem, generated by the lack of labeled data in a target domain, researchers have studied the behavior of machine learning methods in cross-domain settings BIBREF2 , BIBREF11 , BIBREF10 and came up with various domain adaptation techniques BIBREF12 , BIBREF5 , BIBREF6 , BIBREF9 . In cross-domain classification, a classifier is trained on data from a source domain and tested on data from a (different) target domain. The accuracy of machine learning methods is usually lower in the cross-domain setting, due to the distribution gap between different domains. However, researchers proposed several domain adaptation techniques by using the unlabeled test data to obtain better performance BIBREF13 , BIBREF14 , BIBREF15 , BIBREF16 , BIBREF7 . Interestingly, some recent works BIBREF10 , BIBREF17 indicate that string kernels can yield robust results in the cross-domain setting without any domain adaptation. In fact, methods based on string kernels have demonstrated impressive results in various text classification tasks ranging from native language identification BIBREF18 , BIBREF19 , BIBREF20 , BIBREF21 and authorship identification BIBREF22 to dialect identification BIBREF23 , BIBREF17 , BIBREF24 , sentiment analysis BIBREF10 , BIBREF25 and automatic essay scoring BIBREF26 . As long as a labeled training set is available, string kernels can reach state-of-the-art results in various languages including English BIBREF19 , BIBREF10 , BIBREF26 , Arabic BIBREF27 , BIBREF20 , BIBREF17 , BIBREF24 , Chinese BIBREF25 and Norwegian BIBREF20 . Different from all these recent approaches, we use unlabeled data from the test set in a transductive setting in order to significantly increase the performance of string kernels. In our recent work BIBREF28 , we proposed two transductive learning approaches combined into a unified framework that improves the results of string kernels in two different tasks. In this paper, we provide a formal and detailed description of our transductive algorithm and present results in cross-domain English polarity classification. The paper is organized as follows. Related work on cross-domain text classification and string kernels is presented in Section SECREF2 . Section SECREF3 presents our approach to obtain domain adapted string kernels. The transductive transfer learning method is described in Section SECREF4 . The polarity classification experiments are presented in Section SECREF5 . Finally, we draw conclusions and discuss future work in Section SECREF6 . 

Question: What is a string kernel?
Answer: String kernel is a technique that uses character n-grams to measure the similarity of strings