Abstract: Addressing the issues of domain-specificity, imbalance, and nested entities in crop pest and disease data, which lead to low recognition accuracy of general models, a crop  disease  and pest entity recognition model incorporating a dual-dictionary approach is proposed. Firstly, the original character data and vocabulary data are introduced into the LE-RoBERTa module and GC-SoftLexicon module, respectively, two independent character vectors are obtained after  enhancement processing. Then, the fused character vectors are input into the BiLSTM encoding layer and CRF decoding layer to obtain the optimal entity sequence output. Experimental results show that the model achieves an F1 -score of 95.56% on the constructed crop  disease  and pest entity dataset, effectively recognizing crop disease and pest entities.

Key words: named entity recognition, crop diseases and pests, agricultural dictionary, word fusion, attention mechanisim