TY - GEN
T1 - Estimation of Precedence Relations to Deal with Regional Complaint Reports
AU - Yamaguchi, Kohei
AU - Mine, Tsunenori
N1 - Funding Information:
This work was supported in part by Grant-in-Aid for Scientific Research proposal numbers (JP21H00907, JP20H01728, JP20H04300, JP19KK0257).
Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - A system in which citizens and the government work together to solve regional issues is known as Government 2.0. To promote this system, the collection of regional issues through mobile crowd sensing and collaborative IoT is being promoted. On the other hand, although prioritization is essential to solve the collected issues, conventional methods only classify the issues and do not identify the precedence relations between the issues. In addition, the latest deep learning models have not been applied to this task. In this study, we apply BERT to the task to identify the priorities of the collected issues based on the safety and security of citizens. We conduct experiments on a data set of regional complaint citizen reports. Experimental results illustrate that the BERT (fine-Tuned approach) outperformed the other baseline methods even in the case of data sets with small vocabulary and biases among priority labels, such as the one in this task.
AB - A system in which citizens and the government work together to solve regional issues is known as Government 2.0. To promote this system, the collection of regional issues through mobile crowd sensing and collaborative IoT is being promoted. On the other hand, although prioritization is essential to solve the collected issues, conventional methods only classify the issues and do not identify the precedence relations between the issues. In addition, the latest deep learning models have not been applied to this task. In this study, we apply BERT to the task to identify the priorities of the collected issues based on the safety and security of citizens. We conduct experiments on a data set of regional complaint citizen reports. Experimental results illustrate that the BERT (fine-Tuned approach) outperformed the other baseline methods even in the case of data sets with small vocabulary and biases among priority labels, such as the one in this task.
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U2 - 10.1109/ICA54137.2021.00008
DO - 10.1109/ICA54137.2021.00008
M3 - Conference contribution
AN - SCOPUS:85127710431
T3 - Proceedings - 2021 IEEE International Conference on Agents, ICA 2021
SP - 7
EP - 12
BT - Proceedings - 2021 IEEE International Conference on Agents, ICA 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE International Conference on Agents, ICA 2021
Y2 - 13 December 2021 through 15 December 2021
ER -