Words are Malleable: Computing Semantic Shifts in Political and Media Discourse
Table 4. Results of different word stability measures in summarizing the viewpoints.
['Method', 'Precision', 'Recall', '[ITALIC] F1']
This section answers RQ3. In general, the performance of the Combination method is slightly better than the linear mapping approach. However, the difference is not statistically significant. The F1 score achieved using the Combination method is 0.75 which is reasonably good and indicates that the annotators were able to detect the viewpoints using the provided summaries. The results show that the linear mapping method performs better than the Neighbor-based approach in the summarization task. This result is in contrast with the results achieved in the classification task. The summarization task is done on the New York Times dataset, while the classification task is performed on the UK parliamentary proceedings. In the parliamentary proceedings, the viewpoints are more apparent as neighbors of a given word generally serve as reliable descriptors of the viewpoint. Therefore, the Neighbor-based approach which is solely based on the similarity of the neighbors in two spaces performs better than the linear mapping method in the classification task.