Sentence Level Recurrent Topic Model: Letting Topics Speak for Themselves
Table 2: Classification results of different models and different topic numbers K. For 20Newsgroup, the measure is accuracy; for Wiki10+, the measure is Micro-F1 score. Higher values mean better classification results.
['[ITALIC] Dataset\\  [ITALIC] Model', '20Newsgroup  [ITALIC] K=128', '20Newsgroup  [ITALIC] K=256', 'Wiki10+  [ITALIC] K=128', 'Wiki10+  [ITALIC] K=256']
From the table, we can see that SLRTM is the best model under each setting on both datasets. We can further find that the embedding based methods (Doc-NADE, GMNTM and SLRTM) generate better document representations than other models, demonstrating the representative power of neural networks based on distributed representations. In addition, when the training data is larger (i.e., with more sentences per document as Wiki10+), GMNTM generates worse topical information than Doc-NADE while our SLRTM outperforms Doc-NADE, showing that with sufficient data, SLRTM is more effective in topic modeling since topic coherence is further constrained for each sentence.