Learning Relational Dependency Networks for Relation Extraction
Table 8: Advice Results comparing models trained without (-Adv) and with advice (+Adv).
['Relation', 'AUC ROC -Adv', 'AUC ROC +Adv', 'Recall -Adv', 'Recall +Adv']
The use of advice improves or matches the performance of using only joint learning. The key impact of advice can be mostly seen in the improvement of recall in several relations. This clearly shows that using human advice patterns allows us to extract more relations effectively making up for noisy or less number of training examples. in that humans can be more than mere labelers by providing useful advice to learning algorithms that can improve their performance. Thus Q4 can be answered affirmatively.