Computer Science ›› 2021, Vol. 48 ›› Issue (6): 241-245.doi: 10.11896/jsjkx.200600011

Special Issue: Natural Language Processing

• Artificial Intelligence • Previous Articles     Next Articles

Joint Question Answering Model Based on Knowledge Representation

LIU Xiao-long, HAN Fang, WANG Zhi-jie   

  1. School of Information Science and Technology,Donghua University,Shanghai 201620,China
  • Received:2020-06-01 Revised:2020-07-29 Online:2021-06-15 Published:2021-06-03
  • About author:LIU Xiao-long,born in 1994,postgra-duate.His main research interests include natural language process and knowledge based question answering.(13122331373@163.com)
    HAN Fang,born in 1981,Ph.D,professor,Ph.D supervisor.Her main research interests include intelligence system andneurodynamics.
  • Supported by:
    National Natural Science Foundation of China(11972115),Special Funds for Basic Research Business Expenses of Central Colleges and Universities and “Inspirational Plan” of Donghua University(18D210402).

Abstract: Question answering system based on knowledge base aims to extract answers directly from the knowledge base by parsing users’ natural language question sentences.Currently,most knowledge based question answering models follow the two steps of entity detection and relationship recognition,but such methods ignore the structural information contained in the know-ledge base and the connection between the two tasks.In this paper,a joint question answering model based on knowledge representation is proposed.First,the knowledge representation model is used to map the entities and relationships in the knowledge base to a low-dimensional vector space,then the question sentences are embedded into the same vector space through neural network,and the entities in the question sentences are detected at the same time.The semantic similarity between knowledge base triples and question sentences is measured in the vector space,so that knowledge base embedding and multi-task learning are introduced into the task of knowledge based question answering.The experimental results show that the proposed model can greatly improve the training speed,and the accuracies of entity detection and relationship recognition task reach the mainstream level.It is proved that knowledge embedding and multi-task learning can improve the performance of knowledge based question answe-ring task.

Key words: Knowledge base embedding, Knowledge base question answering, Multi-task learning

CLC Number: 

  • TP311
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