Joint Entity and Relation Extraction Model Based on Entity-Pair Specific Attention Mechanism
Entity and relation extraction is a key technology to automatically build large-scale knowledge graphs from massive text data.Considering the effect of the entity on the discrimination of relation types,this paper proposes a joint entity and relation extraction model based on entity-pair specific attention mechanism(EPSA).First,the entity recognition is completed based on Bi-directional Long Short-Term Memory(Bi-LSTM)and Conditional Random Fields(CRF).Then the extracted entities are combined into entity-pairs and transformed into a unified embedding.The sentence representation is obtained by the entity-pair specific attention mechanism plus the entity-pair embed-ding.And finally,the relation extraction is completed by the a classification process.Experimental results on NYT and WebNLG datasets show that the proposed method out-performs the baselines by achieving 84.5％and 88.5％F1 value,respectively.
knowledge graphattention mechanismjoint entity and relation extraction
万方数据
维普
