Hierarchical Losses and New Resources for Fine-grained Entity Typing and Linking
Table 6: MAP of entity-level typing in Wikipedia data using TypeNet. The second column shows results using 5% of the total data. The last column shows results using the full set of 344,246 entities.
['Model', 'Low Data', 'Full Data']
We see that both the basic CNN and the CNN+Complex models perform similarly with the CNN+Complex model doing slightly better on the full data regime. We also see that both models get an improvement when adding an explicit hierarchy loss, even before adding in the transitive closure. The transitive closure itself gives an additional increase in performance to both models. In both of these cases, the basic CNN model improves by a greater amount than CNN+Complex. This could be a result of the complex embeddings being more difficult to optimize and therefore more susceptible to variations in hyperparameters. When adding in both the transitive closure and the explicit hierarchy loss, the performance improves further. We observe similar trends when training our models in a lower data regime with ~150,000 examples, or about 5% of the total data.