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During our pilot crowdsourcing annotation experiments, we noticed a low agreement on numerical FEs. Moreover, asking the crowd to label such frequently occurring FEs would represent a considerable overhead, resulting in a higher temporal cost (i.e., more annotations per sentence) and lower overall annotation accuracy. Hence, we opted for the implementation of a rule-based system to detect and normalize numerical expressions. The normalization process takes as input a numerical expression such as a date, a duration, or a score, and outputs a transformation into a standard format suitable for later inclusion into the target KB.
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