Training Temporal Word Embeddings with a Compass
Table 1: Details of NAC-S, NAC-L, MLPC, T1 and T2.
['Data', 'Words', 'Span', 'Slices']
They are both composed of temporal word analogies based on publicly recorded knowledge, partitioned in categories (e.g., President of the USA, Super Bowl Champions). Dataset The size of each slice is very small (less than 130,000 words after pre-processing) and it increases over years.