Making Sense of Word Embeddings
Table 3: Influence of context filtering on disambiguation in terms of F-score. The models were trained on Wikipedia corpus; the w2v is based on weighted pooling and similarity-based disambiguation. All differences between filtered and unfiltered models are significant (p<0.05).
['[EMPTY]', '[BOLD] Full TWSI  [BOLD] w2v', '[BOLD] Full TWSI  [BOLD] JBT', '[BOLD] Balanced TWSI  [BOLD] w2v', '[BOLD] Balanced TWSI  [BOLD] JBT']
The more granular the sense inventory, the better the match between the TWSI and the induced inventory can be established (mind that we map every induced sense to at most one TWSI sense). Therefore, the upper bound of WSD performance is maximal for the most fine-grained inventories.