Computer Science ›› 2019, Vol. 46 ›› Issue (12): 242-249.doi: 10.11896/jsjkx.181102117

• Artificial Intelligence • Previous Articles     Next Articles

Entity and Relationship Joint Extraction Method of Feedback Mechanism

MA Jian-hong, LI Zhen-zhen, ZHU Huai-zhong, WEI Zi-mo   

  1. (School of Artificial Intelligence,Hebei University of Technology,Tianjin 300401,China)
  • Received:2018-11-16 Online:2019-12-15 Published:2019-12-17

Abstract: Entity and relationship extraction are two core tasks in information extraction,and are the important cornerstone of knowledge mapping.At present,entity recognition and relationship extraction usually adopt the method of extracting features and rules manually and realizing them independently in two steps.This method is easy to cause duplicate data preprocessing and error propagation.The two modules are interrelated.Entity recognition is the basis of relationship extraction.The results of entity relationship extraction can also feedback and verify entity information.Therefore,a hybrid neural network model (Mufeedback-Join Model) without adding manual features and with mutual feedback mechanism was proposed to extract entities and their relationships jointly and realize the feedback checking mechanism of entity relationship to entity recognition.The model shares Bi-LSTM feature extraction layer to extract text context features,and introduces attention mechanism to capture key parts based on shared layer features.After decoding the feature,CRF is used to complete the entity sequence labeling task.The shared layer feature and entity feature are input into CNN model to realize entity relationship extraction.Finally,the relationship extraction result loss value is calculated,and the feature extraction layer of loss value feedback correction and the parameters of entity recognition model are combined.In the same hardware environment,this method can shorten the training time of model,improve the accuracy,recall and F1 value of entity and relationship extraction,The F1 value of the joint extraction is improved by 2.91%,the entity identification sub-module F1 is increased by 1.34% on average,and the relationship extraction F1 value is increased by 5.79%.The experimental data show that the joint extraction model can merge two sub-modules to reduce data processing time and error data transmission,and the mechanism of mutual feedback can improve the overall recognition effect.

Key words: Deep learning, Entity recognition, Feedback mechanism, Joint extraction, Relation extraction

CLC Number: 

  • TP391
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