SEmHuS: a semantically embedded humanitarian space
Abstract Humanitarian crises are unpredictable and complex environments, in which access to basic services and infrastructures is not adequately available. Computing in a humanitarian crisis environment is different from any other environment. In humanitarian environments the accessibility to electr...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2023-03-01
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| Series: | Journal of International Humanitarian Action |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s41018-023-00135-4 |
| _version_ | 1797863795439173632 |
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| author | Aladdin Shamoug Stephen Cranefield Grant Dick |
| author_facet | Aladdin Shamoug Stephen Cranefield Grant Dick |
| author_sort | Aladdin Shamoug |
| collection | DOAJ |
| description | Abstract Humanitarian crises are unpredictable and complex environments, in which access to basic services and infrastructures is not adequately available. Computing in a humanitarian crisis environment is different from any other environment. In humanitarian environments the accessibility to electricity, internet, and qualified human resources is usually limited. Hence, advanced computing technologies in such an environment are hard to deploy and implement. Moreover, time and resources in those environments are also limited and devoted for life-saving activities, which makes computing technologies among the lowest priorities for those who operate there. In humanitarian crises, interests and preferences of decision-makers are driven by their original languages, cultures, education, religions, and political affiliations. Hence, decision-making in such environments is usually hard and slow because it solely depends on human capacity in absence of proper computing techniques. In this research, we are interested in overcoming the above challenges by involving machines in humanitarian response. This work proposes and evaluates a text classification and embedding technique to transform historical humanitarian records from human-oriented into a machine-oriented structure (in a vector space). This technique allows machines to extract humanitarian knowledge and use it to answer questions and classify documents. Having machines involved in those tasks helps decision-makers in speeding up humanitarian response, reducing its cost, saving lives, and easing human suffering. |
| first_indexed | 2024-04-09T22:41:22Z |
| format | Article |
| id | doaj.art-39a8dafc02b24321bfdff9320a8cc272 |
| institution | Directory Open Access Journal |
| issn | 2364-3412 2364-3404 |
| language | English |
| last_indexed | 2024-04-09T22:41:22Z |
| publishDate | 2023-03-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of International Humanitarian Action |
| spelling | doaj.art-39a8dafc02b24321bfdff9320a8cc2722023-03-22T12:06:13ZengSpringerOpenJournal of International Humanitarian Action2364-34122364-34042023-03-018112310.1186/s41018-023-00135-4SEmHuS: a semantically embedded humanitarian spaceAladdin Shamoug0Stephen Cranefield1Grant Dick2Department of Information Science, University of OtagoDepartment of Information Science, University of OtagoDepartment of Information Science, University of OtagoAbstract Humanitarian crises are unpredictable and complex environments, in which access to basic services and infrastructures is not adequately available. Computing in a humanitarian crisis environment is different from any other environment. In humanitarian environments the accessibility to electricity, internet, and qualified human resources is usually limited. Hence, advanced computing technologies in such an environment are hard to deploy and implement. Moreover, time and resources in those environments are also limited and devoted for life-saving activities, which makes computing technologies among the lowest priorities for those who operate there. In humanitarian crises, interests and preferences of decision-makers are driven by their original languages, cultures, education, religions, and political affiliations. Hence, decision-making in such environments is usually hard and slow because it solely depends on human capacity in absence of proper computing techniques. In this research, we are interested in overcoming the above challenges by involving machines in humanitarian response. This work proposes and evaluates a text classification and embedding technique to transform historical humanitarian records from human-oriented into a machine-oriented structure (in a vector space). This technique allows machines to extract humanitarian knowledge and use it to answer questions and classify documents. Having machines involved in those tasks helps decision-makers in speeding up humanitarian response, reducing its cost, saving lives, and easing human suffering.https://doi.org/10.1186/s41018-023-00135-4Machine learningNatural language processingWord embeddingClass embeddingHumanitarian response |
| spellingShingle | Aladdin Shamoug Stephen Cranefield Grant Dick SEmHuS: a semantically embedded humanitarian space Journal of International Humanitarian Action Machine learning Natural language processing Word embedding Class embedding Humanitarian response |
| title | SEmHuS: a semantically embedded humanitarian space |
| title_full | SEmHuS: a semantically embedded humanitarian space |
| title_fullStr | SEmHuS: a semantically embedded humanitarian space |
| title_full_unstemmed | SEmHuS: a semantically embedded humanitarian space |
| title_short | SEmHuS: a semantically embedded humanitarian space |
| title_sort | semhus a semantically embedded humanitarian space |
| topic | Machine learning Natural language processing Word embedding Class embedding Humanitarian response |
| url | https://doi.org/10.1186/s41018-023-00135-4 |
| work_keys_str_mv | AT aladdinshamoug semhusasemanticallyembeddedhumanitarianspace AT stephencranefield semhusasemanticallyembeddedhumanitarianspace AT grantdick semhusasemanticallyembeddedhumanitarianspace |