Paper Abstract and Keywords |
Presentation |
2021-02-18 10:30
Classification of disaster tweets for damage assessment, and improvement by feature analysis Yuto Oikawa, Ptaszynski Michal, Fumito Masui (KIT) NLC2020-22 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
In extracting tweets useful in rescue missions during disasters, previous research have focused on extracting tweets containing specific addresses or locations. We assume that tweets without addresses can also be useful for disaster relief as the location can be inferred or written indirectly. In this study, we focus on extracting tweets from users who directly experienced the disaster (tweets with high directness) and classified them into three classes using BERT, based on the assumption that the tweets, when provided to rescue teams, can be useful for evaluating the disaster situation. Additionally, we performed feature analysis of the training data, which helped us update the annotation criteria, and improve the classification efficacy. The results were satisfying enough to be considered for application in efficient information extraction during disasters. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Disaster Information / Rescue Request / Twitter / BERT / Document Classification / Text Mining / / |
Reference Info. |
IEICE Tech. Rep., vol. 120, no. 374, NLC2020-22, pp. 7-12, Feb. 2021. |
Paper # |
NLC2020-22 |
Date of Issue |
2021-02-11 (NLC) |
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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NLC2020-22 |
Conference Information |
Committee |
NLC |
Conference Date |
2021-02-18 - 2021-02-18 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Online |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
The 17th Text Analytics Symposium |
Paper Information |
Registration To |
NLC |
Conference Code |
2021-02-NLC |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Classification of disaster tweets for damage assessment, and improvement by feature analysis |
Sub Title (in English) |
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Keyword(1) |
Disaster Information |
Keyword(2) |
Rescue Request |
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Twitter |
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BERT |
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Document Classification |
Keyword(6) |
Text Mining |
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1st Author's Name |
Yuto Oikawa |
1st Author's Affiliation |
Kitami Institute of Technology (KIT) |
2nd Author's Name |
Ptaszynski Michal |
2nd Author's Affiliation |
Kitami Institute of Technology (KIT) |
3rd Author's Name |
Fumito Masui |
3rd Author's Affiliation |
Kitami Institute of Technology (KIT) |
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Speaker |
Author-1 |
Date Time |
2021-02-18 10:30:00 |
Presentation Time |
25 minutes |
Registration for |
NLC |
Paper # |
NLC2020-22 |
Volume (vol) |
vol.120 |
Number (no) |
no.374 |
Page |
pp.7-12 |
#Pages |
6 |
Date of Issue |
2021-02-11 (NLC) |
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