Compass-aligned Distributional Embeddings for Studying Semantic Differences across Corpora
Table 12: STAT and Accuracy (MP@1) for the analogies in T1.
['Model', 'Subset', 'MP@1', 'STAT']
However, TW2V performs badly in this test set and predicts dynamic answers more than 90% of the time. The poor performance of TW2V on dynamic analogies suggests that the temporal embeddings are highly dynamic due to their low quality and the insufficient training process. TW2V answers 30% of the time as SW2V, Moreover, we may expect that the static answer may “hide” sometimes the dynamic correct one. (its rank is 2): it corresponds to a 5% drop in MP@1 on dynamic analogies. TW2V may have the opposite problem: its word representations are too dynamic; as a consequence, it gives less dynamic answers to static analogies than it should, but this may be an advantage with dynamic analogies.