Shamoug Aladdin, Cranefield Stephen, Dick Grant
Department of Information Science, University of Otago, Dunedin, New Zealand.
J Int Humanit Action. 2023;8(1):3. doi: 10.1186/s41018-023-00135-4. Epub 2023 Mar 7.
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.
The online version contains supplementary material available at 10.1186/s41018-023-00135-4.
人道主义危机是不可预测且复杂的环境，在这种环境中无法充分获得基本服务和基础设施。在人道主义危机环境中进行计算与其他任何环境都不同。在人道主义环境中，电力、互联网和合格人力资源的可及性通常有限。因此，在这样的环境中先进的计算技术很难部署和实施。此外，这些环境中的时间和资源也有限，且都用于救生活动，这使得计算技术对于在那里开展工作的人员来说是最不优先考虑的事项。在人道主义危机中，决策者的利益和偏好受其母语、文化、教育、宗教和政治归属的驱动。因此，在这样的环境中决策通常既困难又缓慢，因为在缺乏适当计算技术的情况下，决策完全依赖于人的能力。在本研究中，我们有兴趣通过让机器参与人道主义应对来克服上述挑战。这项工作提出并评估了一种文本分类和嵌入技术，以将历史人道主义记录从面向人的结构转换为面向机器的结构（在向量空间中）。这种技术使机器能够提取人道主义知识并用于回答问题和对文档进行分类。让机器参与这些任务有助于决策者加快人道主义应对速度、降低成本、拯救生命并减轻人类痛苦。
在线版本包含可在10.1186/s41018 - 023 - 00135 - 4获取的补充材料。
