| A multitask transfer learning solution | Entity type features: We hypothesize that the entity types and subtypes of the relation arguments are also more likely to be associated with specific relation types. |
| A multitask transfer learning solution | We refer to the set of features that contain the entity type or subtype of an argument as “arg-NE” features. |
| A multitask transfer learning solution | 4.4 Imposing entity type constraints |
| Conclusions and future work | In this paper, we applied multitask transfer learning to solve a weakly-supervised relation extraction problem, leveraging both labeled instances of auxiliary relation types and human knowledge including hypotheses on feature generality and entity type constraints. |
| Conclusions and future work | We also leveraged additional human knowledge about the target relation type in the form of entity type constraints. |
| Conclusions and future work | Experiment results on the ACE 2004 data show that the multitask transfer learning method achieves the best performance when we combine human guidance with automatic general feature selection, followed by imposing the entity type constraints. |
| Experiments | Finally, TL-NE builds on top of TL-comb and uses the entity type constraints to refine the predictions. |
| Introduction | ditional human knowledge about the entity type constraints on the relation arguments, which can usually be derived from the definition of a relation type. |
| Task definition | For example, we may be given the entity type restrictions on the two relation arguments. |
| Task definition | Nodes that represent the arguments are also labeled with the entity type , subtype and mention type as defined by ACE. |