@article{nedellec-etal-2026-epop,
    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",
    editor = "Piperidis, Stelios  and
      Bel, N{\'u}ria  and
      van den Heuvel, Henk  and
      Ide, Nancy  and
      Krek, Simon  and
      Toral, Antonio",
    journal = "International Conference on Language Resources and Evaluation",
    volume = "main",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "ELRA Language Resource Association",
    url = "https://preview.aclanthology.org/ingest-lrec/2026.lrec-main.103/",
    pages = "1331--1340",
    abstract = "We introduce the EPOP (Epidemiomonitoring of Plants) corpus, a new annotated resource for structured information extraction in the domain of plant health epidemiology. The corpus consists of translated news reports that reflect real-world phytosanitary monitoring scenarios. It includes annotations for named entities (e.g. Plant, Pest, Vector, Disease, Dissemination Pathway), identity coreferences, and both binary and complex n-ary relations that represent key events such as Transmits or Causes, along with their modalities. A distinctive feature of EPOP is its normalization layer where mentions of species and geographical locations are linked to canonical identifiers in the NCBI Taxonomy and GeoNames, enabling semantic disambiguation and integration with external knowledge bases. As the first publicly available corpus of its kind, EPOP presents a realistic and challenging benchmark, with high linguistic variability, entity role ambiguity, and long-distance relations. We report baseline results on core tasks (named entity recognition, normalization (entity-linking), and relation extraction) using both fine-tuned BERT-based models and hard-prompted large language models. These experiments demonstrate the utility of EPOP while also identifying areas for improvement, particularly in the extraction of complex relations. The corpus is released under an open license, to support research in environmental NLP, crop protection, and knowledge graph enrichment."
}