Jointly Learning Word Embeddings and Latent Topics
Table 4. Topic coherence evaluation using the PMI metric with different numbers of top words.
['[EMPTY]', '[ITALIC] T=5', 'T = 10', 'T = 15', 'T = 20']
We can see that our STE model generally improves the coherence of the learned topics. Compared with BTM, our STE model incorporates the semantic relationship between words which is learned from word embeddings to improve the quality of topics.