Segmentation Similarity and Agreement
Table 1: Stability of mean (with standard deviation) values of WD and S in three different scenarios, each defining the: probability of a false positive (FP), false negative (FN), or both. Each scenario varies the range of internal segment sizes (e.g., (20,30)). Low standard deviation and similar within-scenario means demonstrates low sensitivity to variations in internal segment size.
['[EMPTY]', '[BOLD] Scenario 1: FN,  [ITALIC] p=0.5 (20,30)', '[BOLD] Scenario 1: FN,  [ITALIC] p=0.5 (15,35)', '[BOLD] Scenario 2: FP,  [ITALIC] p=0.5 (20,30)', '[BOLD] Scenario 2: FP,  [ITALIC] p=0.5 (15,35)', '[BOLD] Scenario 3: FP and FN,  [ITALIC] p=0.5 (20,30)', '[BOLD] Scenario 3: FP and FN,  [ITALIC] p=0.5 (15,35)']
When Pevzner and Hearst \shortcitePevznerHearst:2002 proposed WD, they demonstrated that it was not as sensitive as Pk to variations in the size of segments inside a segmentation. To show this, they simulated how WD performs upon a segmentation comprised of 1000 segments with four different uniformly distributed ranges of internal segment sizes (keeping the mean at approximately 25 units) in comparison to a hypothesis segmentation with errors (false positives, false negatives, and both) uniformly distributed within segments [PevznerHearst:2002, pp. 11–12]. 10 trials were performed for each segment size range and error probability, with 100 hypotheses generated per trial.