Breaking Sticks and Ambiguities with Adaptive Skip-gram
Table 7: Fuzzy Normalized Mutual Information and Fuzzy B-Cubed metric values for task-13 of Semeval-2013 competition. See text for details.
['[BOLD] MODEL', '[BOLD] FUZZY-NMI', '[BOLD] FUZZY-B-CUBED']
This task evaluates Word Sense Induction systems by performing fuzzy clustering comparison, i.e. in the gold standard each context could be assigned to several meanings with some score indicating confidence of the assignment. Fuzzy-NMI measures the alignment of two clustering and it is independent of the cluster sizes. It is suitable to measure how well the model captures rare senses. On the contrary Fuzzy-B-Cubed is sensitive to the cluster sizes. So it reflects the performance of the system on a dataset where the clusters have almost the same frequency. Results of MSSG, NP-MSSG and