Fine-Grained Entity Typing for Domain Independent Entity Linking
Table 5: Accuracy on the Unseen-Mentions test set. Our model substantially outperforms neural entity linking models in this setting.
['Model', 'Training', 'Test']
Our model achieves the best performance in this setting, better than all baselines. Notably, the GRU+Attn model, which achieves state-of-the-art performance on WikilinksNED, performs poorly, underperforming the Most Frequent Entity baseline. The simpler CBoW+word2vec model with the frozen entity embeddings shows slightly better performance than the GRU+Attn model, implying that the model suffers from overfitting to the training data. The poor performance of these two models trained on the domain-specific data suggests that dealing with unseen mention-entity pairs is challenging even for these vector-based approaches trained with similar domain data, indicating that entity generalization is a major factor in entity linking performance. The GRU+Attn model trained on the Wikipedia data also performs poorly.