Temporal Event Knowledge Acquisition via Identifying Narratives
Table 2: Precision of narratives based on human annotation
['Narratives', 'Seed', 'Bootstrapped']
We determined that a text is a correct narrative if both annotators labeled it as a narrative. We can see that on average, the rule-based classifier achieves the precision of 88% on initializing seed narratives and the statistical classifier achieves the precision of 84% on bootstrapping new ones. Using narratology based features enables the statistical classifier to extensively learn new narrative, and meanwhile maintain a high precision.