Improving distant supervision using inference learning
Table 2: Evaluation using held-out data
['[EMPTY]', '[BOLD] Unfiltered Prec.', '[BOLD] Unfiltered Rec.', '[BOLD] Unfiltered F1', '[BOLD] Random-reduced Prec.', '[BOLD] Random-reduced Rec.', '[BOLD] Random-reduced F1', '[BOLD] PRA-reduced Prec.', '[BOLD] PRA-reduced Rec.', '[BOLD] PRA-reduced F1']
Overall results, averaged across all relations with maximum recall, are shown in the top portion of the table and indicate that applying PRA improves performance. Although the highest precision is obtained using the Unfiltered classifier, the PRA-reduced classifier leads to the best recall and F1. Performance of the Random-reduced classifier indicates that the improvement is not simply due to a change in the bias in the data but that the examples it contains lead to an improved model. The PRA-reduced classifier produces the best results for the majority of relations and always increases recall compared to Unfiltered.