Word sense induction using word embeddings and community detection in complex networks
Table 2: Performance of our best methods evaluated using instances that were labeled with just one sense. Best results are marked in bold. Note that the proposed CN approach outperforms traditional approaches when using both F1 and Fuzzy NMI criteria. The results for the SemCor Ranked are not shown because, in the analysis considered only one possible sense, SemCor Ranked and SemCor MFS are equivalent.
['System', '[ITALIC] ω', '[ITALIC] k', 'F1', 'Fuzzy NMI', 'Fuzzy B-Cubed']
In the second evaluation, only instances labeled with just one sense were considered. Considering F1 to evaluate the sense induction performance, our method outperformed all baselines, but it could not outperform the best participants methods. In the cluster evaluation, conversely, our best method displayed the best performance when compared to almost all other participants. Only two methods (One Sense and SemCor MFS) outperformed our CN approach when considering the instance performance evaluation (as measured by the Fuzzy B-Cubed index). Regarding the best k used to generate networks, we have found that, as in the previous case, in most of the configuration of parameters, the best results were obtained when the fully connected network was used.