Entity boundaries, confusable entity types and surface forms absent from the training data all make it harder to extract structured information from agricultural pest and disease texts. We examined whether single-step adversarial training of word embeddings with the fast gradient method (FGM) improves entity recognition when the BERT-CRF inference architecture is left unchanged. On the official 13-type AgCNER corpus, with exact matching of character span and entity type, we compared a feature-based CRF, BiLSTM-CRF, BERT-CRF and BERT-CRF+FGM, training each neural model with three fixed random seeds. BERT-CRF+FGM reached mean test precision, recall and F1 of 94.37%, 95.10% and 94.73%. Its F1 w... More >