Jointly Learning Word Embeddings and Latent Topics
Table 6. Document classification results.
['[BOLD] Model', '[BOLD] Accuracy', '[BOLD] Precision', '[BOLD] Recall', '[BOLD] F-measure']
Good results are also obtained for the TWE model, which suggests that the quality of document representations can clearly benefit from combining topics models with word embeddings. Nevertheless, compared to TWE, our model can take advantage of the interaction between topics and word embeddings to improve both, and is thus able to outperform the TWE model. The performance of GPU-DMM is not as good as expected. Unfortunately, the documents in 20Newsgroups are not short texts.