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. Attached Files Name Description MIMEType Size Downloads Title Improving Personal Health Mention detection on Twitter using Permutation Based Word Representation Learning Author(s) Khan, Pervaiz Iqbal 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 Document type: Conference Paper Collections: Faculty of Science, Engineering and Built Environment School of Information Technology Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Tue, 24 Nov 2020, 06:54:40 EST Tue, 24 Nov 2020, 14:21:59 EST Wed, 25 Nov 2020, 04:00:35 EST Wed, 25 Nov 2020, 05:04:36 EST Tue, 01 Dec 2020, 09:14:40 EST Tue, 01 Dec 2020, 09:20:27 EST Tue, 01 Dec 2020, 15:00:27 EST Sat, 26 Dec 2020, 01:30:58 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 41 Abstract Views, 1 File Downloads  -  Detailed Statistics Created: Tue, 24 Nov 2020, 14:21:59 EST

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