Ontology-based Information Extraction is crucial to translate natural language documents into Linked Data. This connection supports consumers in navigating documents and semantically related data. However, the performances of automated information extraction systems are far from being perfect, and rely heavily on human intervention, either to create heuristics, to annotate examples for inferring models, or to interpret or validate patterns emerging from data.
In this paper, we apply different Active Learning strategies to Information Extraction (IE) from licenses in English, with highly repetitive text, few annotated or unannotated examples available, and very fine precision needed. We show that the most popular approach to active learning, i.e., uncertainty sampling for instance selection, does not provide a good performance in this setting. We show that we can obtain a similar effect to that of density-based methods using uncertainty sampling, by just reversing the ranking criterion, and choosing the most certain instead of the most uncertain instances.
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