Learning Topics using Semantic Locality
TABLE I: LDA and RBM performance evaluation
['[BOLD] mAP', '[BOLD] OMDb LDA', '[BOLD] OMDb RBM', '[BOLD] Reuters LDA', '[BOLD] Reuters RBM', '[BOLD] 20NewsGroup LDA', '[BOLD] 20NewsGroup RBM']
In the first experiment, we investigate the topic modeling performance between LDA and RBM. For the training of the LDA model, the training iteration is 15 and the number of generated topics is 500 which are as the same as the RBM model. The RBM outperforms the LDA in all datasets. For example, using the mAP5 evaluation, the RBM is 30.22% greater than the LDA in OMDb dataset, 18.18% greater in Reuters dataset and 25.25% greater in 20NewsGroup dataset. To have a fair comparison, the RBM model here is based on word only features. In the next we will show the including word pairs can further improve its mAP score.