@inproceedings{lai-king-etal-2026-foodclimner,
title = "{F}ood{C}lim{NER}: A Manually Annotated Corpus for Food Practice Entities Related to Climate Change",
author = {La{\"i}-king, Mathieu and
Ferr{\'e}, Arnaud and
Del{\'e}ger, Louise and
Le Bodo, Yann and
Chaix, Estelle},
editor = "Schimanski, Tobias and
Usmanova, Aida and
Morio, Gaku and
Ni, Jingwei and
Dutia, Kalyan and
Vaghefi, Seyed Saeid and
Stammbach, Dominik and
Leippold, Markus",
booktitle = "Proceedings of the 3rd Workshop on Natural Language Processing Meets Climate Change ({C}limate{NLP} 2026)",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.climatenlp-1.11/",
pages = "143--157",
ISBN = "979-8-89176-465-1",
abstract = "Climate change is reshaping food systems and consumer practices, creating new challenges for food safety. Understanding how people modify their eating habits in response to climate disruptions and sustainability challenges is critical for assessing emerging microbiological risks. We present FoodClimNER, a new named entity recognition (NER) corpus containing extracts from open-access scientific articles on food science in the context of climate change, with manual annotations made by an expert. We define annotation guidelines with expert definitions of food practice entities and climate change entities. We evaluate baselines and a custom prompting method with recent LLMs on this task in a zero-shot setting. Results indicate that expert-level entity types and the presence of nested and discontinuous mentions make this a challenging testbed, with significant room for improving zero-shot methods. We also provide a detailed analysis of the importance of different parts of our custom prompt."
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<abstract>Climate change is reshaping food systems and consumer practices, creating new challenges for food safety. Understanding how people modify their eating habits in response to climate disruptions and sustainability challenges is critical for assessing emerging microbiological risks. We present FoodClimNER, a new named entity recognition (NER) corpus containing extracts from open-access scientific articles on food science in the context of climate change, with manual annotations made by an expert. We define annotation guidelines with expert definitions of food practice entities and climate change entities. We evaluate baselines and a custom prompting method with recent LLMs on this task in a zero-shot setting. Results indicate that expert-level entity types and the presence of nested and discontinuous mentions make this a challenging testbed, with significant room for improving zero-shot methods. We also provide a detailed analysis of the importance of different parts of our custom prompt.</abstract>
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%0 Conference Proceedings
%T FoodClimNER: A Manually Annotated Corpus for Food Practice Entities Related to Climate Change
%A Laï-king, Mathieu
%A Ferré, Arnaud
%A Deléger, Louise
%A Le Bodo, Yann
%A Chaix, Estelle
%Y Schimanski, Tobias
%Y Usmanova, Aida
%Y Morio, Gaku
%Y Ni, Jingwei
%Y Dutia, Kalyan
%Y Vaghefi, Seyed Saeid
%Y Stammbach, Dominik
%Y Leippold, Markus
%S Proceedings of the 3rd Workshop on Natural Language Processing Meets Climate Change (ClimateNLP 2026)
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-465-1
%F lai-king-etal-2026-foodclimner
%X Climate change is reshaping food systems and consumer practices, creating new challenges for food safety. Understanding how people modify their eating habits in response to climate disruptions and sustainability challenges is critical for assessing emerging microbiological risks. We present FoodClimNER, a new named entity recognition (NER) corpus containing extracts from open-access scientific articles on food science in the context of climate change, with manual annotations made by an expert. We define annotation guidelines with expert definitions of food practice entities and climate change entities. We evaluate baselines and a custom prompting method with recent LLMs on this task in a zero-shot setting. Results indicate that expert-level entity types and the presence of nested and discontinuous mentions make this a challenging testbed, with significant room for improving zero-shot methods. We also provide a detailed analysis of the importance of different parts of our custom prompt.
%U https://aclanthology.org/2026.climatenlp-1.11/
%P 143-157
Markdown (Informal)
[FoodClimNER: A Manually Annotated Corpus for Food Practice Entities Related to Climate Change](https://aclanthology.org/2026.climatenlp-1.11/) (Laï-king et al., ClimateNLP 2026)
ACL