What You Say and How You Say it: Joint Modeling of Topics and Discourse in Microblog Conversations
Table 2: Cv coherence scores for latent topics produced by different models. The best result in each column is highlighted in bold. Our joint model Topic+Disc achieves significantly better coherence scores than all the baselines (p<0.01, paired test).
['[BOLD] Models', '[ITALIC] K=50 TREC', '[ITALIC] K=50 TWT16', '[ITALIC] K=100 TREC', '[ITALIC] K=100 TWT16']
For the topic coherence, we adopt the Cv scores measured via the open-source Palmetto toolkit as our evaluation metric. Cv scores assume that the top N words in a coherent topics (ranked by likelihood) tend to co-occur in the same document and have shown comparable evaluation results to human judgments Röder et al. The values range from 0.0 to 1.0 and higher scores indicate better topic coherence. We can observe that: