Leveraging External Knowledge for Out-Of-Vocabulary Entity Labeling
Table 2: F1 and accuracy results for SC under different OOV settings and candidate generation strategies.
['Strategy', '[ITALIC] F1 OOV (0%)', 'Accuracy OOV (0%)', '[ITALIC] F1 OOV (100%)', 'Accuracy OOV (100%)']
We train SC in all six datasets, and evaluate it on a fixed test set. We do not retrain the embeddings used by SC. The difference between HM and MLP is negligible, and we can conclude that the MLP learns the mappings properly. This means as well that MLP is bounded by the performance of HM: it is only as good as the underlying probability distribution, and will asymptotically perform the same, regardless of the OOV percentage. In contrast, our approach assigns a hypernym-based namespace to the data. In the 100% OOV case, this approach vastly outperforms both strategies. This is likely due to the fact that it is able to inject its own, semantically-related, slot key namespace into SC. This also would explain the lower performance in the 0% OOV setting.