Ensemble method to joint inference for knowledge extraction
作者：
Highlights：
• Traditionally, the probably approximately correct (PAC) learning refers the single concept class. We discuss the PAC framework of the multiple tasks in the joint inference model. And we extend PAC learning to multi-concept classes.
• We present an ensemble learning approach to joint inference on the three NLP sub-tasks. We explain how to combine those weak learners to a strong ability and present the dynamic weighted combination method in the ensemble joint inference model.
• Our Ensemble Markov Logic Networks (EMLNs) address the problem of the Markov Logic Networks intractable dealing with the large scale data. Experiments show that this approach leads to a higher precision and recall than that of pipeline approaches.
摘要
•Traditionally, the probably approximately correct (PAC) learning refers the single concept class. We discuss the PAC framework of the multiple tasks in the joint inference model. And we extend PAC learning to multi-concept classes.•We present an ensemble learning approach to joint inference on the three NLP sub-tasks. We explain how to combine those weak learners to a strong ability and present the dynamic weighted combination method in the ensemble joint inference model.•Our Ensemble Markov Logic Networks (EMLNs) address the problem of the Markov Logic Networks intractable dealing with the large scale data. Experiments show that this approach leads to a higher precision and recall than that of pipeline approaches.
论文关键词：Ensemble learning,Joint inference,Knowledge extraction,Markov logic network
论文评审过程：Received 25 October 2016, Revised 16 March 2017, Accepted 18 April 2017, Available online 22 April 2017, Version of Record 26 April 2017.
论文官网地址：https://doi.org/10.1016/j.eswa.2017.04.036
