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SEmHuS: a semantically embedded humanitarian space.

Aladdin Shamoug1, Stephen Cranefield1, Grant Dick1

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This summary is machine-generated.

This study introduces a machine learning approach to analyze historical humanitarian data, enabling faster and more efficient crisis response. By transforming records into a machine-readable format, it aids decision-making and saves lives.

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Area of Science:

  • Computer Science
  • Information Science
  • Humanitarian Studies

Background:

  • Humanitarian crises present unique challenges, including limited infrastructure, resources, and accessibility to technology.
  • Decision-making in these complex environments is often slow and hindered by human factors and a lack of computational support.

Purpose of the Study:

  • To overcome challenges in humanitarian response by integrating machine involvement.
  • To develop and evaluate a text classification and embedding technique for processing historical humanitarian records.

Main Methods:

  • A text classification and embedding technique was proposed and evaluated.
  • Historical humanitarian records were transformed from a human-oriented to a machine-oriented structure within a vector space.

Main Results:

  • The technique enables machines to extract knowledge from humanitarian records.
  • Machines can answer questions and classify documents, supporting data-driven insights.

Conclusions:

  • Involving machines in humanitarian response can significantly speed up operations.
  • This approach can reduce costs, save lives, and alleviate human suffering through enhanced decision-making.