DAWT: Densely Annotated Wikipedia Texts across multiple languages
Table 7: Accuracy of Semantic Analogy
['[BOLD] Relation', '[BOLD] GloVe Word dimensionality 50', '[BOLD] GloVe Word dimensionality 100', '[BOLD] GloVe Word dimensionality 200', '[BOLD] GloVe Word dimensionality 300', '[BOLD] DAWT Entity dimensionality 50', '[BOLD] DAWT Entity dimensionality 300', '[BOLD] DAWT Entity dimensionality 1000']
In comparison, it also shows the accuracy from GloVe word embeddings with vector sizes of 50, 100, 200, and 300. Entity embeddings have better performance with vector size of 50. As we increase vector size, word embeddings perform significantly better and outperform entity embeddings when the vector size is 200 or higher. The degraded performance of entity embeddings may due to less training data, since our entity embeddings were obtained from 2.2B tokens, where GloVe’s word embeddings were obtained from 6B tokens.