Breaking Sticks and Ambiguities with Adaptive Skip-gram
Table 3: Adjusted rand index (ARI) for word sense induction task for different datasets. Here we use the test subset of WWSI dataset. See sec. 5.4 for details.
['[BOLD]  MODEL', '[BOLD] SE-2007', '[BOLD] SE-2010', '[BOLD] SE-2013', '[BOLD] WWSI']
AdaGram consistently outperforms the concurrent approaches on all datasets and achieves significant improvement on the test part of WWSI dataset. One may see that nonparametric version of MSSG delivers consistently worse performance than MSSG with number of prototypes fixed to 3. This suggests that the ability to discover different word meanings is rather limited for NP-MSSG. The fact that AdaGram substantially outperforms NP-MSSG indicates that more principled Bayesian nonparametric approach is more suitable for the task of word-sense induction.