Semantic flow in language networks
Table 2: Accuracy rate and p-value obtained for the classification substaks. Only the best results are shown among all considered classifiers and thresholds. We considered the simplified unweighted version of the Markov networks to extract motifs.
['[BOLD] Subtask', '[BOLD] Acc.', '[BOLD] Threshold', '[ITALIC] p [BOLD] -value']
Given the low accuracy rates obtained with the unweighted strategy, we analyzed if the simplified unweighted version was able to provide a better characterization. In this case, the weakest edges were removed before the extraction of motifs. We considered the thresholding ranging between 0.01 and 0.20. The main idea here is to remove less important links between communities. All obtained results turned out to be significant. All previous accuracy rates were improved. Interestingly, a high discrimination rate (91.6%) was obtained when discriminating investigative and philosophy books. These results suggest that the threshold is an important pre-processing step here, given that it can boost the performance of the classification by a large margin.