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Besides the fact datasets, we also keep track of confidence scores and generate additional datasets accord- ingly. Therefore, it is possible to filter facts that are not considered as confident by setting a suitable threshold. When processing a sentence, our pipeline outputs two different scores for each FE, stemming from the entity linker and the supervised classifier. We merge both signals by calculating the F-score between them, as if they were representing precision and recall, in a fashion similar to the standard classification metrics. The final score can be then produced via an aggregation of the single FE scores in multiple ways, namely: (a) arithmetic mean; (b) weighted mean based on core FEs (i.e., they have a higher weight than extra ones); (c) harmonic mean, weighted on core FEs as well.
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