Semi-supervised knowledge extractionfor detection of drugs and their effects
Table 3: Classification results for substances and effects, including the “rest” category
['[BOLD] # of seeds', '[BOLD] Recall', '[BOLD] Precision', '[BOLD] F1']
Thus, within the extracted terminology, we have manually labeled about 100 entries as neither drugs nor effects, and we have used them as candidates. This has been done to evaluate the effectiveness of using the parameter θc to avoid classifying these terms as either substances or effects. Performance-wise, this resulted in few more false positives given by terms erroneously assigned to the substance and effect classes, when instead these 100 candidates should ideally all be discarded. We can observe that, when we include in the evaluation also those data that are neither substances nor effects, with no training data other than the original seeds, and operating only on the thresholds, the precision drops significantly.