UnibucKernel: A kernel-based learning method for complex word identification
Table 3: Regression results on the three data sets of the English monolingual track of the 2018 CWI Shared Task. The methods are evaluated in terms of the mean absolute error (MAE). The reported results are obtained after the competition.
['Data Set', 'Kernel', 'Mean Absolute Error', 'Post-Competition Rank']
Results. The systems are evaluated in terms of the mean absolute error (MAE). As in the classification task, we can observe that the RBF kernel provides generally better results than the linear kernel. On two data sets, English News and English WikiNews, we obtain better MAE values than all the systems that participated in the competition. Indeed, the best MAE on English News reported during the competition is 0.0510, and we obtain a smaller MAE (0.0492) using the RBF kernel. Similarly, with a MAE of 0.0667 for the RBF kernel, we surpass the top system on English WikiNews, which attained a MAE of 0.0674 during the competition. On the third data set, English Wikipedia, we attain the second-best score (0.0805), after the top system, that obtained a MAE of 0.0739 during the competition. Compared to the classification task, we report better post-competition rankings in the regression task. This could be explained by two factors. First of all, the number of participants in the regression task was considerably lower. Second of all, we believe that ν-SVR is a very good regressor which is not commonly used, surpassing alternative regression methods in other tasks as well, e.g. image difficulty prediction Ionescu et al.