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.

本文研究了使用简单而有效的传导学习方法以及自我训练方法来优化字符串核技术在一些文本分类任务中的表现，并通过将字符串核适应于测试集中的样本，取得了在英语情感极性分类和阿拉伯语方言识别中显著更好的准确率。

通过适应测试集来提升字符串核算法在情感分析和阿拉伯方言识别中的结果