Linear Algebraic Structure of Word Senses, with Applications to Polysemy
Table 7: Performance of different vectors in the WSI task of SemEval 2010. The parameter k is the number of clusters used in the methods. Rows are divided into two blocks, the first of which shows the results of the competitors, and the second shows those of our algorithm. Best results in each block are in boldface.
['Method', 'V-Measure', 'F-Score']
Given a word and its example texts, our algorithm uses a Bayesian analysis dictated by our theory to compute a vector uc for the word in each context c and and then applies k-means on these vectors, with the small twist that sense vectors are assigned to nearest centers based on inner products rather than Euclidean distances. Results. Our approach outperforms the results by \newcitehuang2012improving and \newciteneelakantan2014efficient. When compared to \newcitemu2016geometry, for the case with 2 centers, we achieved better V-measure but lower F-score, while for 5 centers, we achieved lower V-measure but better F-score.