Chinese Journal of Network and Information Security ›› 2018, Vol. 4 ›› Issue (9): 44-51.doi: 10.11959/j.issn.2096-109x.2018074

• Papers • Previous Articles     Next Articles

Relation extraction based on CNN and Bi-LSTM

Xiaobin ZHANG(),Fucai CHEN,Ruiyang HUANG   

  1. China National Digital Switching System Engineering &Technological R&D Center,Zhengzhou 450002,China
  • Revised:2018-08-20 Online:2018-09-15 Published:2018-10-15
  • Supported by:
    The National Natural Science Fund for Creative Research Groups Project(61521003);The National Key Research and Development Program of China(2016YFB0800101)


Relation extraction aims to identify the entities in the Web text and extract the implicit relationships between entities in the text.Studies have shown that deep neural networks are feasible for relation extraction tasks and are superior to traditional methods.Most of the current relation extraction methods apply convolutional neural network (CNN) and long short-term memory neural network (LSTM) methods.However,CNN just considers the correlation between consecutive words and ignores the correlation between discontinuous words.On the other side,although LSTM takes correlation between long-distance words into account,the extraction features are not sufficiently extracted.In order to solve these problems,a relation extraction method that combining CNN and LSTM was proposed.three methods were used to carry out the experiments,and confirmed the effectiveness of these methods,which had some improvement in F1 score.

Key words: relation extraction, convolution neural networks, long short-term memory, attention mechanism

CLC Number: 

No Suggested Reading articles found!