---
title: "EPOP: A Benchmark Corpus for Assessing NLP — Nedellec 2026"
description: "Plant health epidemiology has received little attention in NLP, mainly because no annotated resources capture how pest and disease outbreaks are actually…"
authors: ["Claire Nedellec", "Marine Courtin", "Xinzhi Yao", "Marie Grosdidier", "Isabelle Pieretti", "Sandy Duperier", "Robert Bossy"]
year: 2026
venue: "Proceedings of the Fifteenth Language Resources and Evaluation Conference"
source: "https://aclanthology.org/2026.lrec-1.103/"
doi: "10.63317/4in2fpefq4pz"
licence: "CC BY-NC 4.0 (ELRA, LREC proceedings)"
licence_url: "https://creativecommons.org/licenses/by-nc/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/nedellec-2026-epop-benchmark-corpus-assessing-nlp/"
---
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# EPOP: A Benchmark Corpus for Assessing NLP Models on Structured Information Extraction in Plant Health

**Authors:** Claire Nedellec, Marine Courtin, Xinzhi Yao, Marie Grosdidier, Isabelle Pieretti, Sandy Duperier, Robert Bossy · **Year:** 2026 · **Venue:** Proceedings of the Fifteenth Language Resources and Evaluation Conference · **Licence:** [CC BY-NC 4.0 (ELRA, LREC proceedings)](https://creativecommons.org/licenses/by-nc/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.lrec-1.103/) · [PDF](https://aclanthology.org/2026.lrec-1.103.pdf) · [DOI 10.63317/4in2fpefq4pz](https://doi.org/10.63317/4in2fpefq4pz) · [OpenAlex](https://openalex.org/W7160207802)

**Citations:** 1 citations (OpenAlex, 2026-10-14)

## Summary
Plant health epidemiology has received little attention in NLP, mainly because no annotated resources capture how pest and disease outbreaks are actually reported. The authors address this with EPOP (Epidemiomonitoring of Plants), which they present as the first openly available corpus for structured information extraction in this field. The texts come from the French ESV Platform's international monitoring repository. They are news-style reports, originally in many languages, that were machine-translated into English and cleaned. Plant-health experts annotated them double-blind, with adjudication, following iteratively refined guidelines. The annotation has four layers: entities, binary and n-ary relations with modality (negation or hypothesis), identity coreference, and normalization of organisms to NCBI Taxonomy and of places to GeoNames. Baselines for NER, entity linking and relation extraction use fine-tuned BERT-based models and hard-prompted LLMs. The results show that the benchmark is usable but difficult, and complex n-ary relations are the weakest point. EPOP matters because it supports surveillance systems that can be checked against the source text and fed into knowledge graphs.

## Key points
- **Data sources:** From a backlog of about 100,000 ESV Platform documents, the authors took a pool of the 1,000 most recently processed ones. These cover 15 priority organisms, such as Xylella fastidiosa, Popillia japonica and Tomato brown rugose fruit virus. The documents were translated from 26 languages with Google Translate and cleaned with Trafilatura.
- **Entities and relations:** There are seven entity types: Pest, Plant, Disease, Pest Vector, Dissemination pathway, Location and Date. Typed binary relations include Found_on, Transmits, Causes, Located_in, Detected_on, Affects and Dispersed_by. N-ary events have between 2 and 6 arguments.
- **Novel features:** The authors describe EPOP as the first plant-health corpus to annotate relation modality. It also annotates discontinuous entities and identity coreference, adapting the coreference approach of the Bacteria Biotope corpus.
- **Annotation process:** Each document was pre-annotated by dictionary lookup. Experts then annotated it double-blind, disagreements were adjudicated, and pattern-based checks flagged common errors.
- **Release:** The corpus and its train/dev splits are published under an open license on Recherche Data Gouv (doi:10.57745/YKSEPY and 10.57745/ZDNOGF). The annotation guidelines are on HAL, and the data also feeds the PestCLEF 2026 task.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/en/shelves/linguistics/)

Topics: [Information extraction](https://md.research.elamur.ai/free/en/topics/information-extraction/) · [Evaluation & benchmarks](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{nedellec2026epop,
  title = {{EPOP: A Benchmark Corpus for Assessing NLP Models on Structured Information Extraction in Plant Health}},
  author = {Nedellec, Claire and Courtin, Marine and Yao, Xinzhi and Grosdidier, Marie and Pieretti, Isabelle and Duperier, Sandy and Bossy, Robert},
  booktitle = {Proceedings of the Fifteenth Language Resources and Evaluation Conference},
  year = {2026},
  doi = {10.63317/4in2fpefq4pz},
  url = {https://doi.org/10.63317/4in2fpefq4pz}
}
```

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