A Novel Document Generation Process for Topic Detection based on Hierarchical Latent Tree Models
Table 3: Average compactness scores.
['[EMPTY]', 'NIPS-1k', 'NIPS-5k', 'NIPS-10k', 'News-1k', 'News-5k', 'NYT', 'AI']
We see that HLTA-c achieved the highest compactness score in all cases. It also achieved the highest coherence score in all cases except for News-1k. The differences between the scores for HLTA-c and those for the LDA-based methods are often large. On the AI dataset, for instance, HLTA-c achieved a coherence score of -16.18 and a compactness score of 0.299. The corresponding scores for nHDP are -18.66 and 0.144 respectively.