Effective Modeling of Encoder-Decoder Architecture for Joint Entity and Relation Extraction
Table 6: % errors for wrong ordering and entity mismatch.
['Model', 'NYT29 Order', 'NYT29 Ent1', 'NYT29 Ent2', 'NYT24 Order', 'NYT24 Ent1', 'NYT24 Ent2']
The relation tuples extracted by a joint model can be erroneous for multiple reasons such as: (i) extracted entities are wrong; (ii) extracted relations are wrong; (iii) pairings of entities with relations are wrong. To see the effects of the first two reasons, we analyze the performance of HRL and our models on entity generation and relation generation separately. For entity generation, we only consider those entities which are part of some tuple. For relation generation, we only consider the relations of the tuples. Our proposed models perform better than HRL on both tasks. Comparing our two models, PtrNetDecoding performs better than WordDecoding on both tasks, although WordDecoding achieves higher F1 scores in tuple extraction. This suggests that PtrNetDecoding makes more errors while pairing the entities with relations. WordDecoding generates fewer errors than the other two models in all the categories and thus achieves the highest F1 scores on both datasets.