A Novel Cascade Binary Tagging Framework for Relational Triple Extraction
Table 4: Results on relational triple elements.
['Element', 'NYT  [ITALIC] Prec.', 'NYT  [ITALIC] Rec.', 'NYT  [ITALIC] F1', 'WebNLG  [ITALIC] Prec.', 'WebNLG  [ITALIC] Rec.', 'WebNLG  [ITALIC] F1']
For NYT, the performance on E1 and E2 are consistent with that on (E1, R) and (R, E2), demonstrating the effectiveness of our proposed framework in identifying both subject and object entity mentions. We also find that there is only a trivial gap between the F1-score on (E1, E2) and (E1, R, E2), but an obvious gap between (E1, R, E2) and (E1, R)/(R, E2). It reveals that most relations for the entity pairs in extracted triples are correctly identified while some extracted entities fail to form a valid relational triple. In other words, it implies that identifying relations is somehow easier than identifying entities for our model.