Semi-supervised knowledge extractionfor detection of drugs and their effects
Table 2: Classification results for substances and effects, varying the number of seeds
['[BOLD] # of seeds', '[BOLD] Recall', '[BOLD] Precision', '[BOLD] F1']
Even though the training set is limited to a small number of entries, the results are interesting: with only 6 seeds, the proposed methodology achieves a F1 score close to 0.88 (on the 2 classes - Substance and Effect). With the aim of monitoring the diffusion of new substances, the result is quite promising, since it is able to detect unknown substances without human supervision.