@inproceedings{jumle-etal-2026-retrieving,
title = "Retrieving Climate Change Adaptation Concepts from World Heritage Management Plans in the {N}etherlands",
author = "Jumle, Vedant and
Agaoglu, Orhan and
Mol, Laurens and
Giron{\'e}s, Edgar Salas and
Bai, Nan",
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-1.13/",
pages = "170--182",
ISBN = "979-8-89176-465-1",
abstract = "Monitoring climate change adaptation (CCA) in the heritage sector, especially in the context of World Heritage (WH) management, requires systematic analyses of policy documents for CCA strategies, a process that is difficult to scale. We ask whether automated retrieval systems can prioritise expert annotation effort: given a CCA concept, can a system rank passages of a management plan so that those an expert would annotate appear near the top, without any in-domain supervision? This objective is formulated as an information retrieval problem with a new benchmark dataset on climate-adaptive heritage management strategies, created from expert-labelled text chunks in official Management Plans of 11 Dutch WH properties. Four retrieval approaches (BM25, a bi-encoder, a dense retriever with reranker, and a cross-encoder reranker) are evaluated and compared. The cross-encoder reranker achieves the strongest performance across labels. An LLM-as-judge evaluation further shows that top-ranked unlabelled chunks could be relevant, suggesting that the existing annotations are precise but not exhaustive. Per-label analysis identifies which concepts are automatically retrievable and which remain beyond the reach of general-purpose retrieval. Our benchmark establishes a foundation for scalable mapping and monitoring of climate change adaptation in the context of heritage management."
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<abstract>Monitoring climate change adaptation (CCA) in the heritage sector, especially in the context of World Heritage (WH) management, requires systematic analyses of policy documents for CCA strategies, a process that is difficult to scale. We ask whether automated retrieval systems can prioritise expert annotation effort: given a CCA concept, can a system rank passages of a management plan so that those an expert would annotate appear near the top, without any in-domain supervision? This objective is formulated as an information retrieval problem with a new benchmark dataset on climate-adaptive heritage management strategies, created from expert-labelled text chunks in official Management Plans of 11 Dutch WH properties. Four retrieval approaches (BM25, a bi-encoder, a dense retriever with reranker, and a cross-encoder reranker) are evaluated and compared. The cross-encoder reranker achieves the strongest performance across labels. An LLM-as-judge evaluation further shows that top-ranked unlabelled chunks could be relevant, suggesting that the existing annotations are precise but not exhaustive. Per-label analysis identifies which concepts are automatically retrievable and which remain beyond the reach of general-purpose retrieval. Our benchmark establishes a foundation for scalable mapping and monitoring of climate change adaptation in the context of heritage management.</abstract>
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%0 Conference Proceedings
%T Retrieving Climate Change Adaptation Concepts from World Heritage Management Plans in the Netherlands
%A Jumle, Vedant
%A Agaoglu, Orhan
%A Mol, Laurens
%A Gironés, Edgar Salas
%A Bai, Nan
%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 jumle-etal-2026-retrieving
%X Monitoring climate change adaptation (CCA) in the heritage sector, especially in the context of World Heritage (WH) management, requires systematic analyses of policy documents for CCA strategies, a process that is difficult to scale. We ask whether automated retrieval systems can prioritise expert annotation effort: given a CCA concept, can a system rank passages of a management plan so that those an expert would annotate appear near the top, without any in-domain supervision? This objective is formulated as an information retrieval problem with a new benchmark dataset on climate-adaptive heritage management strategies, created from expert-labelled text chunks in official Management Plans of 11 Dutch WH properties. Four retrieval approaches (BM25, a bi-encoder, a dense retriever with reranker, and a cross-encoder reranker) are evaluated and compared. The cross-encoder reranker achieves the strongest performance across labels. An LLM-as-judge evaluation further shows that top-ranked unlabelled chunks could be relevant, suggesting that the existing annotations are precise but not exhaustive. Per-label analysis identifies which concepts are automatically retrievable and which remain beyond the reach of general-purpose retrieval. Our benchmark establishes a foundation for scalable mapping and monitoring of climate change adaptation in the context of heritage management.
%U https://aclanthology.org/2026.climatenlp-1.13/
%P 170-182
Markdown (Informal)
[Retrieving Climate Change Adaptation Concepts from World Heritage Management Plans in the Netherlands](https://aclanthology.org/2026.climatenlp-1.13/) (Jumle et al., ClimateNLP 2026)
ACL