Words are Malleable: Computing Semantic Shifts in Political and Media Discourse
Table 3. Results of classification of speeches to parties using different word stability measures. We consider SVM+General as our baseline.
['Method', 'Precision', 'Recall', '[ITALIC] F1']
In general, the results indicate that the proposed word stability measures help in discriminating documents. Moreover, two other observations can be made from the results. First, expanding documents, even with a general embedding can improve performance of the classifiers. Second, the Combination method performs better than the other approaches. The linear mapping approach does not outperform the baseline. The higher accuracy of the Combination method shows that, although the linear mapping approach does not improve the performance of the classifier, when it is combined with the Neighbor-based method, the performance is improved.