Learning Topic-Sensitive Word Representations
Table 3: GAP scores on LS-SE07 and LS-CIC sets. For SGE + C we use the context embeddings to disambiguate the substitutions. Improvements over the best baseline (MSSG) are marked ▲ at p
['[EMPTY]', '[EMPTY]', 'LS-SE07 Dimension', 'LS-SE07 Dimension', 'LS-SE07 Dimension', 'LS-CIC Dimension', 'LS-CIC Dimension', 'LS-CIC Dimension']
Our proposed models outperform both SGE and MSSG in both evaluation sets, with more pronounced improvements for LS-CIC. We further observe that our expected method is more robust and performs better for all embedding sizes.