Hierarchical Losses and New Resources for Fine-grained Entity Typing and Linking
Table 7: Accuracy on entity linking in MedMentions. Maximum recall is 81.82% because we use an imperfect alias table to generate candidates. Normalized scores consider only mentions which contain the gold entity in the candidate set. Mention tfidf is csim from Section 4.3.
['Model', 'original', 'normalized']
None of these models incorporate transitive closure information, due to difficulty incorporating it in our candidate generation, which we leave to future work. The Normalized metric considers performance only on mentions with an alias table hit; all models have 0 accuracy for mentions otherwise. We also report the overall score for comparison in future work with improved candidate generation. We see that incorporating structure information results in a 1.1% reduction in absolute error, corresponding to a ~6% reduction in relative error on this large-scale dataset.