Distributed representation of multi-sense words: A loss-driven approach
Table 2: Results for the Spearman rank correlation (ρ×100).
['Dataset', 'Model', '[ITALIC] d', 'maxSimC', 'avgSimC', 'avgSim']
For all the vector dimensions, LDMI performs better than the competing approaches on the maxSimC metric. For the GOBW dataset, LDMI shows an average improvement of about 13% over the NP-MSSG and 10% over the MSSG on the maxSimC metric. The average is taken over all vector dimensions. This shows the advantage of LDMI over the competing approaches. For the avgSimC metric, LDMI performs at par with the competing approaches. The other approaches are not as effective in identifying the correct number of senses, leading to noisy clusters and hence, poor performance on the maxSimC metric. LDMI also performs better than LDMI-SK on both maxSimC and avgSimC, demonstrating the effectiveness of the clustering approach employed by LDMI over spherical k-means.