Compass-aligned Distributional Embeddings for Studying Semantic Differences across Corpora
Table 7: British-American spelling test with words with frequency higher than 20 in the corpus (BAW1)
['Algorithm', 'HITS@5', 'HITS@10']
We looked at the top-5 and top-10 neighbors, and we thus evaluated the HITS@5 and HITS@10 on both BAW1 and BAW2. Another point is that with the increase of the frequency, both models become better with the mappings; these results confirm what was introduced in the experiment with temporal analogies: frequency is a key element to generate a good representation.