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<p>Natural Language Processing (NLP) offers significant opportunities to support the Sustainable Development Goals (SDGs), including Zero Hunger. While many NLP applications have been documented for SDGs such as healthcare and education, its application to food security remains largely unexplored. This paper addresses this knowledge gap through a comprehensive scoping review focused on NLP for food security policymaking. Six key application areas were identified: (1) Early warning systems for food insecurity, (2) Understanding public discourse on food related issues, (3) Knowledge generation and management from food policy and program documents, (4) Understanding dietary habits, (5) Food item classification, and (6) Addressing data gaps in food security statistics and crisis response. However, limited deployment hinders real-world impact. Establishing authentic partnerships from the outset will be essential for the successful and sustained implementation of NLP projects that advance progress toward ending hunger and achieving food security and improved nutrition for all.</p>

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The role of language technology and artificial intelligence in food security policymaking

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月4日

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  • Marieke Meeske,
  • Frans Cruijssen,
  • Chris van der Lee,
  • Emiel Krahmer

摘要

Natural Language Processing (NLP) offers significant opportunities to support the Sustainable Development Goals (SDGs), including Zero Hunger. While many NLP applications have been documented for SDGs such as healthcare and education, its application to food security remains largely unexplored. This paper addresses this knowledge gap through a comprehensive scoping review focused on NLP for food security policymaking. Six key application areas were identified: (1) Early warning systems for food insecurity, (2) Understanding public discourse on food related issues, (3) Knowledge generation and management from food policy and program documents, (4) Understanding dietary habits, (5) Food item classification, and (6) Addressing data gaps in food security statistics and crisis response. However, limited deployment hinders real-world impact. Establishing authentic partnerships from the outset will be essential for the successful and sustained implementation of NLP projects that advance progress toward ending hunger and achieving food security and improved nutrition for all.