Neural Models for Documents with Metadata
Table 5: Performance of various models on the Yahoo answers dataset with 250 topics and a 5,000-word vocabulary. SAGE did not finish in 72 hours so we omit it from this table.
['Model', 'Ppl. ↓', 'NPMI (int.) ↑', 'NPMI (ext.) ↑', 'Sparsity ↑']
These results closely match those for the IMDB dataset, with our model having higher perplexity but also higher internal coherence than LDA. As with IMDB, the use of word vectors improves coherence, both internally and externally, but again at the cost of worse perplexity. Surprisingly, our model without a background term actually has the best external coherence on this dataset, but as described in the main paper, these tend to give high weight primarily to common words, and are more repetitive as a result.