Improving the results of string kernels in sentiment analysis and Arabic dialect identification by adapting them to your test set
Table 1: Multi-source cross-domain polarity classification accuracy rates (in %) of our transductive approaches versus a state-of-the-art (sota) baseline based on string kernels Giménez-Pérez et al. (2017), as well as SST Bollegala et al. (2013) and KE-Meta Franco-Salvador et al. (2015). The best accuracy rates are highlighted in bold. The marker * indicates that the performance is significantly better than the best baseline string kernel according to a paired McNemar’s test performed at a significance level of 0.01.
['Method', 'DEK→B', 'BEK→D', 'BDK→E', 'BDE→K']
Results in multi-source setting. Both the transductive presence bits string kernel (¨K0/1) and the transductive intersection kernel (¨K∩) obtain better results than their original counterparts. When we employ the transductive kernel classifier (TKC), we obtain even better results. On all domains, the accuracy rates yielded by the transductive classifier are more than 1.5% better than the best baseline. For example, on the Books domain the accuracy of the transductive classifier based on the presence bits kernel (84.1%) is 2.1% above the best baseline (82.0%) represented by the intersection string kernel. Remarkably, the improvements brought by our transductive string kernel approach are statistically significant in all domains.