Combined Model to Extract Entities and Relations Based on Sharing Parameter
Authors: 
Wei Zhuo, Wang FanAuthors Info & Claims
Pages 780 - 785
Abstract
This paper uses the depth learning model of sharing parameter to extract entities and relationships. The problems of pipeline model error propagation and ignoring the internal relationship between subtasks, a parameter sharing model is proposed, which uses graph convolution neural network based on syntax to capture the structural information of text. The model combined with the parameter sharing mode will be introduced in detail. The motivation of designing the model, the special labeling strategy, the structure of the model, the experimental setup and the analysis of the experimental results will be introduced respectively. From the experimental results, it can be seen that the hybrid model achieves better results in the public data set.
References
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Index Terms
- Combined Model to Extract Entities and Relations Based on Sharing Parameter
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Published In
September 2019
803 pages
ISBN:9781450372985
DOI:10.1145/3366194
Copyright © 2019 ACM.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 20 September 2019
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- Research-article
- Research
- Refereed limited
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RICAI 2019
RICAI 2019: 2019 International Conference on Robotics, Intelligent Control and Artificial Intelligence
September 20 - 22, 2019
Shanghai, China
Acceptance Rates
RICAI '19 Paper Acceptance Rate 140 of 294 submissions, 48%;
Overall Acceptance Rate 140 of 294 submissions, 48%
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