Jointly Learning Word Embeddings and Latent Topics
Table 2. Spearman correlation ρ×100 for the SCWS data set.
['[BOLD] Model', '[BOLD] Similarity Metrics', '[ITALIC] ρ×100']
Following previous work, we use the Spearman correlation coefficient as the evaluation metric. The STE model performs comparably to the state-of-the-art TWE model, and outperforms the baseline methods. TWE also exploits both topics and embeddings. However, the vectors in the TWE model have twice as many dimensions as those in our model, since each word is represented as the concatenation of a word vector and a topic vector. The STE-Diff variant outperforms STE-Same, for both the AvgSimC and MaxSimC ranking measures.