Joint Extraction of Entity Relations in the Field of Tibetan Medicine Based on Multi-Feature Fusion and Reward-and-Punishment Mechanism
The entity relation joint extraction task refers to extracting semantic relations between entities while iden-tifying named entities.This paper proposes a joint extraction method of entity relations in the Tibetan medicine field based on multi feature fusion and reward-and-punishment mechanism.We adopt the nested entity annotation strategy to break through the limitations of existing annotation methods.The static fusion of category features,dynamic fusion of multi features,and reward-and-punishment mechanisms are applied for feature enhancement and model optimization.The experimental results show that our method is effective and superior to the baseline methods.
Tibetan medicineentity relationjoint extractionmulti-feature fusionreward and punishment mecha-nism
NETL
NSTL
万方数据
维普
