CoType: Joint Extraction of Typed Entities and Relations with Knowledge Bases
Table 6: Performance comparison on relation classification accuracy over ground-truth relation mentions on the three datasets.
['[BOLD] Method', '[BOLD] \xa0NYT', '[BOLD] Wiki-KBP', '[BOLD] BioInfer']
2. Performance on Relation Classification. To test the effectiveness of the learned embeddings in representing type semantics of relation mentions, we compare with other methods on classifying the ground-truth relation mention in the evaluation set by target types CoType achieves superior accuracy compared to all other methods and variants (e.g., obtains over 10% enhancement on both the NYT and BioInfer datasets over the next best method). All compared methods (except for MultiR) simply treat DL as “perfectly labeled” when training models. Comparing CoType-RM with MultiR, superior performance of CoType-RM demonstrates the effectiveness of partial-label loss over multi-instance learning. Finally, CoType outperforms CoType-RM and CoType-TwoStep validates that the propose translation-based embedding objective is effective in capturing entity-relation cross-constraints.