Two-pass Discourse Segmentation with Pairing and Global Features
Table 6: The effect of removing pairing features (−p), removing global features (−g), and removing both (−pg), in comparison with the full model in the first section, across different segmentation frameworks. CRF stands for our standard two-pass segmentation models based on linear-chain CRFs, while LR and SVM stand for two different classifiers in the framework of independent binary classification.
['[BOLD] Model', '[BOLD] Precision', '[BOLD] Recall', '[ITALIC] F1 [BOLD]  score']
The first section lists the performance of our full model in different segmentation frameworks. As can be seen, our full models perform similarly across different frameworks, where the absolute difference in F1 is less than 0.2% and insignificant. This is consistent with \newciteHernault:2010 :SMD:2175352.2175383’s finding that, when a large number of contextual features are incorporated, binary classifiers such as SVM can achieve competitive performance with CRFs. The second section lists the performance of our models with no pairing features (−p). For all three resulting models, CRF−p, LR−p, and SVM−p, their performance is significantly poorer (p