Semantic flow in language networks
Table 1: Accuracy rate and p-value obtained for the classification substaks. Only the best results are shown among all considered classifiers. We considered the unweighted version of the Markov networks to extract motifs.
['[BOLD] Subtask', '[BOLD] Acc.', '[ITALIC] p [BOLD] -value']
In the first experiment, we evaluated if patterns of semantic changes are able to distinguish between children, philosophy or investigative books. We considered problems with two or three classes. In this case, weights were disregarded after the construction of the Markov networks (unweighted version). Considering subtasks encompassing only two classes, only the distinction between children and investigative texts were not significant, with a low accuracy rate. The distinction philosophy books and the other two classes, however, yielded a much better discrimination. These results were found to be significant. When all three classes are discriminated, a low accuracy rate was found (43.8%), even though this still represents a significant result. The low accuracy rate found using the proposed approach is a consequence of a regular behavior found in the Markov chains. In other words, in most of the books, all communities were found to be connected to each other, hampering thus the discriminability of different types of books.