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Outclassing Wikipedia in Open Domain Information Extraction: Weakly Supervised Acquisition of Attributes over Conceptual Hierarchies

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Table 1: Examples of instances within labeled classes extracted from unstructured text, used as input forattribute extraction experiments
Table 3: Coverage of class instances extracted from text and linked to WordNet (used as input in runs Eand Esa respectively), measured as the fraction of WordNet HasInstance instances (used as input in runN) that occur among the class instances (Cvg=coverage)
Figure 1: Accuracy of the attributes extracted forvarious runs, as an average over the entire set of25 target concepts (left graphs) and as an averageover (variable) subsets of the 25 target conceptsfor which some attributes were extracted in eachrun (righ
Table 5: Comparative accuracy of the attributes extracted by various runs, for individual concepts, as anaverage over the entire set of 25 target concepts, and as an average over (variable) subsets of the 25 targetconcepts for which some attributes were ex

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