A Large-Scale Multilingual Disambiguation of Glosses
Table 7: Accuracy (Acc.) and F-Measure (F1) percentages of different systems on the Wikipedia sense clustering datasets.
['[EMPTY]', '[BOLD] 500-pair  [BOLD] Acc.', '[BOLD] 500-pair  [BOLD] F1', '[BOLD] SemEval  [BOLD] Acc.', '[BOLD] SemEval  [BOLD] F1']
As a naive baseline we include a system which clusters all pairs. For comparison we also include the Support Vector Machine classifier of \newciteDandalaetal:2013 exploiting information of Wikipedia in four different languages (Dandala-multilingual). Moreover, NASARI+glosses obtains the best performance overall, outperforming Dandala-multilingual in terms of accuracy in both datasets.