Journal of Guangxi Normal University(Natural Science Edition) ›› 2019, Vol. 37 ›› Issue (4): 53-60.doi: 10.16088/j.issn.1001-6600.2019.04.006

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Remote Supervision Relationship Extraction Based on Encoder and Attention Mechanism

WANG Jian*, ZHENG Qifan, LI Chao, SHI Jing   

  1. College of Information and Computer Engineering, Northeast Forestry University, Harbin Heilongjiang 150040,China
  • Received:2019-02-28 Online:2019-10-25 Published:2019-11-28

Abstract: In information extraction, relation extraction is a key technology to accurately identify the relationships between entities in natural language. Aiming at the problem that the key semantics in the relation extraction model are easy to lose and the basic assumptions of remote supervision are easy to introduce noise data, an ENCODER_ATT relationship extraction model based on remote supervision is proposed. Firstly, the ENCODER model based on the construction of the cyclic neural network extracts the feature memory at the word level and integrates the semantic feature information at the sentence level to ensure that the key features are removed without removing the redundant features. Secondly, attention mechanism is introduced at the sentence level to reduce the influence of noise data on the test results. Based on the actual experimental data, the experiment was carried out and the accuracy-recall rate curve was drawn to prove that the ENCODER_ATT model has a better improvement over the relationship extraction method of the same type.

Key words: relationship extraction, remote supervision, ENCODER, attention mechanism

CLC Number: 

  • TP391.1
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