Paper Abstract and Keywords |
Presentation |
2023-09-21 15:00
Analysis of subtasks for improving the detection accuracy of offensive tweets in multitask learning Ryoichi Sawada, Yu Suzuki (Gifu) DE2023-14 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
There are studies on detecting offensive tweets, but there is a need to further improve the accuracy.One method to improve accuracy is multitask learning.Multitask learning is a method to improve generalization performance by solving multiple related tasks with a model.There are some studies on combining multitask learning with the offensive tweets detection task.However, it is not obvious which subtasks are effective in improving the accuracy of offensive tweets detection.Therefore, in this study, we proposed a task that improves the accuracy of the offensive tweet detection task.We also conducted experiments to verify whether the proposed task is effective in improving the accuracy of the offensive tweet detection task.Experimental results showed that solving the anger emotion detection task simultaneously with the offensive tweet detection task using multitask learning was more effective than solving the random task simultaneously.In addition, the weighted loss function was found to be effective in improving the F value of the simultaneously solved detection task only when the number of data in the simultaneously solved detection task was unbalanced and the F value was very low. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
offensive tweet / weighted loss / multitask learning / / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 123, no. 192, DE2023-14, pp. 19-24, Sept. 2023. |
Paper # |
DE2023-14 |
Date of Issue |
2023-09-14 (DE) |
ISSN |
Online edition: ISSN 2432-6380 |
Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) |
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DE2023-14 |
Conference Information |
Committee |
DE IPSJ-DBS IPSJ-IFAT |
Conference Date |
2023-09-21 - 2023-09-22 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Kitakyushu International Conference Center |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
Bigdata management, information retrieval, knowledge discovery, etc. |
Paper Information |
Registration To |
DE |
Conference Code |
2023-09-DE-DBS-IFAT |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Analysis of subtasks for improving the detection accuracy of offensive tweets in multitask learning |
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offensive tweet |
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weighted loss |
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multitask learning |
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1st Author's Name |
Ryoichi Sawada |
1st Author's Affiliation |
Gifu University (Gifu) |
2nd Author's Name |
Yu Suzuki |
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Gifu University (Gifu) |
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Speaker |
Author-1 |
Date Time |
2023-09-21 15:00:00 |
Presentation Time |
25 minutes |
Registration for |
DE |
Paper # |
DE2023-14 |
Volume (vol) |
vol.123 |
Number (no) |
no.192 |
Page |
pp.19-24 |
#Pages |
6 |
Date of Issue |
2023-09-14 (DE) |
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