One Representation per Word — Does it make Sense for Composition?
Table 5: Performance overview for all parts-of-speech and number of senses, ‡ statistically significant at the p<0.01 level in comparison to the Word Overlap baseline; † statistically significant at the p<0.05 level in comparison to the Word Overlap baseline.
['[BOLD] Symmetric context window of size 1', '[BOLD] Symmetric context window of size 1  [BOLD] Adjective', '[BOLD] Symmetric context window of size 1  [BOLD] Adjective', '[BOLD] Symmetric context window of size 1  [BOLD] Adjective', '[BOLD] Symmetric context window of size 1  [BOLD] Adjective', '[BOLD] Symmetric context window of size 1  [BOLD] Noun', '[BOLD] Symmetric context window of size 1  [BOLD] Noun', '[BOLD] Symmetric context window of size 1  [BOLD] Noun', '[BOLD] Symmetric context window of size 1  [BOLD] Noun', '[BOLD] Symmetric context window of size 1  [BOLD] Verb', '[BOLD] Symmetric context window of size 1  [BOLD] Verb', '[BOLD] Symmetric context window of size 1  [BOLD] Verb', '[BOLD] Symmetric context window of size 1  [BOLD] Verb']
All models substantially outperform the random baseline for any number of senses. Interestingly the word overlap baseline is competitive for all context window sizes. The performance of all other models is roughly in the same ballpark for all parts-of-speech and number of senses, suggesting that they form robust baselines for future models.