Anchor-Free Correlated Topic Modeling: Identifiability and Algorithm
Table 2: Experiment results on the Reuters-21578 corpus.
['[ITALIC] F', 'Coh FastAchor', 'Coh SPA', 'Coh SNPA', 'Coh XRAY', 'Coh AnchorFree', 'SimCount FastAchor', 'SimCount SPA', 'SimCount SNPA', 'SimCount XRAY', 'SimCount AnchorFree', 'ClustAcc FastAchor', 'ClustAcc SPA', 'ClustAcc SNPA', 'ClustAcc XRAY', 'ClustAcc AnchorFree']
From F=3 to 25, the proposed algorithm (AnchorFree) gives very promising results: for the three considered metrics, AnchorFree consistently gives better results compared to the baselines. Particularly, the ClustAcc’s obtained by AnchorFree are at least 30% higher compared to the baselines for all cases. In addition, the single-topic quality of the topics mined by AnchorFree is the highest in terms of coherence scores; the overlaps between topics are the smallest except for F=20 and 25. In this experiment, we can see that XRAY is best in terms of single-topic quality, while AnchorFree is second best when F>6. For SimCount, AnchorFree gives the lowest values when F>6. In terms of clustering accuracy, the topics obtained by AnchorFree again lead to much higher clustering accuracies in all cases.