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.

Retrieval-augmented generation (RAG) is increasingly popular in information extraction (IE), especially in biological and chemical domains where external knowledge is abundant. Existing RAG-enhanced IE systems, however, typically retrieve for every input, even though retrieval may be unnecessary, introduce distracting evidence, and increase inference cost. To address this issue, we propose RAG Switch, a retrieval decision framework for IE that combines difficulty-aware signals with task-grounded supervision. Specifically, we approximate model-specific training difficulty, quantified by Error L2-Norm (EL2N), from inference-time uncertainty signals, and use it to guide retrieval decisions. We then define retrieval necessity directly from instance-level extraction outcomes without retrieval, yielding task-grounded supervision for learning retrieval decision. Experiments on multiple biological and chemical IE benchmarks across diverse IE backbones show that RAG Switch consistently outperforms representative retrieval decision baselines and achieves a strong efficiency–performance trade-off in end-to-end RAG-enhanced IE pipelines.

Generative methods have recently gained traction in biological and chemical named entity recognition for their ability to overcome tagging limitations and better capture entity-rich contexts. However, under a few-shot environment, they struggle with the scarcity of annotated data and the structural complexity of biological and chemical entities—particularly nested and discontinuous ones—leading to incorrect recognition and error propagation during generation. To address these challenges, we propose SCoNE, a Self-Correcting and Noise-Augmented Method for Complex Biological and Chemical Named Entity Recognition. Specifically, we introduce a Noise Augmentation Module to enhance training diversity and guide the model to better learn complex entity structures. Besides, we design a Confidence-based Self-Correction Module that identifies low-confidence outputs and revises them to improve generation robustness. Benefiting from these designs, our method outperforms the baselines by 1.80 and 2.73 F1-score on the CHEMDNER and microbial ecology dataset Florilege, highlighting its effectiveness in biological and chemical named entity recognition.

Understanding how scientific findings are discussed and evaluated over time provides valuable evidence for assessing their reliability. We propose the Rhetorical Citation Graph (RCG), a finding-centric graph model that represents citation events by explicitly linking scientific findings, citing articles, rhetorical functions, and citation contexts. We instantiate this model as the Plant Literature Reliability Network (PLIN), a graph of 262,807 citation events automatically constructed from the plant-health literature using Natural Language Processing methods for citation and finding extraction, and rhetorical stance classification. By integrating provenance data with the rhetorical interpretation expressed in citation contexts, the RCG provides structured evidence for contextual analyses of scientific findings and lays the foundation for longitudinal studies of their evolution, scientific consensus, and reliability. Finally, we illustrate the utility of PLIN through a case study on the curation and enrichment of the EPPO Global Database, a reference knowledge base for plant health.