Attentive Mimicking:Better Word Embeddings by Attending to Informative Contexts
Table 3: Results on the Name Typing dataset for various word frequencies f. The model that uses a linear combination of AM embeddings with skipgram is denoted AM+skip.
['[BOLD] model', '[ITALIC] f=1 acc', '[ITALIC] f=1 F1', '[ITALIC] f∈[2,4) acc', '[ITALIC] f∈[2,4) F1', '[ITALIC] f∈[4,8) acc', '[ITALIC] f∈[4,8) F1', '[ITALIC] f∈[8,16) acc', '[ITALIC] f∈[8,16) F1', '[ITALIC] f∈[16,32) acc', '[ITALIC] f∈[16,32) F1', '[ITALIC] f∈[32,64) acc', '[ITALIC] f∈[32,64) F1', '[ITALIC] f∈[1,100] acc', '[ITALIC] f∈[1,100] F1']
In accordance with results from previous experiments, AM performs drastically better than the baselines for up to 16 occurrences. Notably, the linear combination of skipgram and AM achieves by far the best overall results.