Research on Entity Relationship Extraction Model of Food Public Opinion Based on CNN-BLSTM

被引：0
|
作者：
Wang Q. [1 ,2 ]
Wang H. [1 ]
Zuo M. [1 ,2 ]
Zhang Q. [1 ,2 ]
Wen X. [2 ]
Yuan Y. [2 ]
机构：
[1] National Engineering Laboratory for Agri-Product Quality Traceability, Beijing Technology and Business University, Beijing
[2] School of Computer and Information Engineering, Beijing Technology and Business University, Beijing
来源：
Journal of Food Science and Technology (China) | 2021年 / 39卷 / 02期
关键词：
Attention mechanism; BLSTM; CNN; Entity relation extraction; Location awareness; Semantic role labeling;
D O I：
10.12301/j.issn.2095-6002.2021.02.019
中图分类号：
学科分类号：
摘要：
The extraction of food public opinion entity relationship is the key technology for constructing the map of food sensation knowledge, and it is also an important research topic in the field of information extraction. Aiming at the entity-to-many relationship problem often appearing in food grievances, the location-aware domain-based semantic attention mechanism is introduced to the convolutional neural network (CNN) and location-aware semantic roles attention mechanism is introduced to the bidirectional long short-term memory network (BLSTM). Then, model was constructed based on CNN-BLSTM for food sentiment entity relationship extraction. In this paper, a comparative experiment was carried out on the food moisture dataset. The experimental results showed that the accuracy of the entity relationship extraction model of food public opinion based on CNN-BLSTM was higher than that of the commonly used deep neural network models. From 8.7% to 13.94%, the rationality and effectiveness of the proposed model were verified. © 2021, Editorial Department of Journal of Food Science and Technology. All right reserved.
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页码：152 / 158
页数：6
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