Improving Personal Health Mention detection on Twitter using Permutation Based Word Representation Learning

Khan, Pervaiz Iqbal, Razzak, Imran, Dengel, Andreas and Ahmed, Sheraz 2020, Improving Personal Health Mention detection on Twitter using Permutation Based Word Representation Learning, in ICONIP 2020 : 27th International Conference on Neural Information Processing - Part 1, Springer Nature, Cham, Switzerland, pp. 776-785, doi: 10.1007/978-3-030-63830-6_65.

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Title Improving Personal Health Mention detection on Twitter using Permutation Based Word Representation Learning
Author(s) Khan, Pervaiz Iqbal
Razzak, ImranORCID iD for Razzak, Imran orcid.org/0000-0002-3930-6600
Dengel, Andreas
Ahmed, Sheraz
Conference name ICONIP - Neural Informational Processing. International Conference (27th : 2020 : Bangkok, Thailand)
Conference location Online : Bangkok, Thailand
Conference dates 23 - 27 Nov. 2020
Title of proceedings ICONIP 2020 : 27th International Conference on Neural Information Processing - Part 1
Editor(s) Yang, Haiqin
Leung, Andrew Chi-Sing
Chan, Jonathan H.
Pasupa, Kitsuchart
Kwok, James T.
King, Irwin
Publication date 2020
Series Lecture Notes in Computer Science Book Series (LNCS)
Start page 776
End page 785
Total pages 10
Publisher Springer Nature
Place of publication Cham, Switzerland
Keyword(s) CORE2020 A
Summary Social media has become a substitute for social interaction, thus the amount of medical and clinical-related information on the web is increasing. Monitoring of Personal Health Mentioning (PHM) on social media is an active area of research that predicts whether a given piece of text contains a health condition or not. To this end, the main idea is to consider the usage of disease or symptom words in the text. However, due to their usage in a figurative sense, disease or symptom words may not always indicate the presence of the health condition. Prior work attempts to address this by considering contextual word representations along with the utilization of the sentiment information. However, these methods are unable to capture the complete context in which symptom word is used. In this work, we incorporate permutation-based contextual word representation for the task of health mention detection which captures the context of disease words efficiently, in the given piece of text, and hence improves the performance of the classifier. To evaluate the integrity of the proposed method, we perform experimentation on the public benchmark dataset that shows an improvement of 5.5% in F-score in comparison to the state of the art health mention detection classifier. (Code is available at https://github.com/pervaizniazi/Figurative-Mention).
ISBN 9783030638290
ISSN 0302-9743
1611-3349
Language eng
DOI 10.1007/978-3-030-63830-6_65
Indigenous content off
Field of Research 0801 Artificial Intelligence and Image Processing
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2020, Springer Nature Switzerland AG
Persistent URL http://hdl.handle.net/10536/DRO/DU:30145664

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