A Frustratingly Easy Approach for Entity and Relation Extraction

Understand

End-to-end relation extraction aims to identify named entities and extract relations between them.

• Most recent work models these two subtasks jointly, either by casting them in one structured prediction framework, or performing multi-task learning through shared representations.
• In this work, we present a simple pipelined approach for entity and relation extraction, and establish the new state-of-the-art on standard benchmarks (ACE04, ACE05 and SciERC), obtaining a 1.7%-2.8% absolute improvement in relation F1 over previous joint models with the same pre-trained encoders.
• Our approach essentially builds on two independent encoders and merely uses the entity model to construct the input for the relation model.