Training Temporal Word Embeddings with a Compass
Table 3: MRR and MP for the subsets of static and dynamic analogies of T2. We use MPK in place of MP@K.
['Model', 'Set', 'MRR', 'MP1', 'MP3', 'MP5', 'MP10']
The settings’ parameters are similar to those of \citeauthorSzymanski2017TemporalEmbeddings: longer embeddings of size 100, a window size of 5, 5 negative samples and a very large vocabulary of almost 200k words with at least 5 occurrences over the entire corpus. TW2V and OW2V score much better results than in the previous setting. This is due to the increased size of the input dataset which allows the training process to work well on individual slices of the corpus.