Neural Models for Documents with Metadata
Table 4: Performance of various models on the 20 newsgroups dataset with 20 topics and a 2,000-word vocabulary.
['Model', 'Ppl. ↓', 'NPMI (int.) ↑', 'NPMI (ext.) ↑', 'Sparsity ↑']
Note that these results are not necessarily directly comparable to previously published results, as preprocessing decisions can have a large impact on perplexity. These results show a similar pattern to those on the IMDB data provided in the main paper, except that word vectors do not improve internal coherence on this dataset, perhaps because of the presence of relatively more names and specialized terminology. Also, although the NVDM still has worse perplexity than LDA, the effects are not as dramatic as reported in Srivastava and Sutton Regularization is also more beneficial for this data, with both SAGE and our regularized model obtaining better coherence than LDA.