Agri-NER-Net: Glyph Fusion for Chinese Field Crop Diseases and Pests Named Entity Recognition Network

Abstract

Field crop pest and disease control knowledge texts contain rich core information such as pest and disease descriptions and control measures. However, it can be challenging to build a knowledge graph for field agricultural diseases due to certain domain characteristic, such as the use of specific terminology or pharmaceuticals, and multiple meanings of characters. Based on these analyses, we propose a named entity recognition method called Agri-NER-Net for field crop diseases and pests. The method firstly designs a multigranularity feature approach, combining characters, Chinese character glyphs, and words. Subsequently, we process these features using BiLSTM network pairs to model contextual long-range location-dependent features, and introduce a self-attention mechanism to enhance the model’s long-range dependency extraction capability. Finally, the LCRF (linear-conditional random field) model is used to predict the labelled sequence of target entities. The experimental results prove that the method proposed in this paper demonstrates a more excellent comprehensive recognition effect compared with the current mainstream named entity recognition models.