A lightweight and unsupervised approach for identifying risk events in news articles

Shahsavari, M; Hussain, OK; Saberi, M; Sharma, P

A lightweight and unsupervised approach for identifying risk events in news articles

Shahsavari, M Hussain, OK Saberi, M

Sharma, P

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2023 IEEE International Conference on Data Mining Workshops (ICDMW), 2024, 00, pp. 37-43
Issue Date:: 2024-02-06

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Field	Value	Language
dc.contributor.author	Shahsavari, M
dc.contributor.author	Hussain, OK
dc.contributor.author	Saberi, M https://orcid.org/0000-0002-5168-2078
dc.contributor.author	Sharma, P
dc.contributor.editor	Pedrycz, W
dc.contributor.editor	Wang, J
dc.contributor.editor	He, Y
dc.contributor.editor	Dinh, TN
dc.contributor.editor	Grant, C
dc.contributor.editor	Qiu, M
dc.date	2023-12-04
dc.date.accessioned	2024-05-13T04:33:30Z
dc.date.available	2024-05-13T04:33:30Z
dc.date.issued	2024-02-06
dc.identifier.citation	2023 IEEE International Conference on Data Mining Workshops (ICDMW), 2024, 00, pp. 37-43
dc.identifier.isbn	979-8-3503-8165-8
dc.identifier.issn	2375-9232
dc.identifier.issn	2375-9259
dc.identifier.uri	http://hdl.handle.net/10453/178925
dc.description.abstract	Event identification is important in many different areas of the business world In the supply chain risk management domain timely identification of risk events is vital for ensuring the success of supply chain operations One of the important sources of real time information from across the world is news sources However the analysis of large amounts of daily news cannot be done manually by humans On the other hand extracting the related news is very much dependent on the query or the keyword used in the search engine along with the news content Recent advancements in Artificial Intelligence have opened up opportunities to leverage intelligent techniques for automating this analysis In our paper we introduce a lightweight framework that with only the event s name as input can autonomously learn all the related phrases associated with that event It then employs these phrases to search for relevant news and presents the search engine results with a label indicating their relevance Through this analysis the framework identifies the event s occurrence in the real world
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2023 IEEE International Conference on Data Mining Workshops (ICDMW)
dc.relation.ispartof	2023 IEEE International Conference on Data Mining Workshops
dc.relation.ispartofseries	International Conference on Data Mining Workshops
dc.relation.isbasedon	10.1109/icdmw60847.2023.00014
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	A lightweight and unsupervised approach for identifying risk events in news articles
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Shanghai, China
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2024-05-13T04:33:29Z
pubs.finish-date	2023-12-04
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2023-12-04
pubs.volume	00
dc.location	Piscataway, USA

Abstract:

Event identification is important in many different areas of the business world In the supply chain risk management domain timely identification of risk events is vital for ensuring the success of supply chain operations One of the important sources of real time information from across the world is news sources However the analysis of large amounts of daily news cannot be done manually by humans On the other hand extracting the related news is very much dependent on the query or the keyword used in the search engine along with the news content Recent advancements in Artificial Intelligence have opened up opportunities to leverage intelligent techniques for automating this analysis In our paper we introduce a lightweight framework that with only the event s name as input can autonomously learn all the related phrases associated with that event It then employs these phrases to search for relevant news and presents the search engine results with a label indicating their relevance Through this analysis the framework identifies the event s occurrence in the real world

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/178925