TRADITIONAL CHINESE MEDICINE NAMED ENTITY RECOGNITION BASED ON DOMAIN KNOWLEDGE GRAPH ENHANCEMENT AND LATTICE-LSTM

Aiming at the problems in existing methods for enhancing entity recognition models through lexicon construction in Traditional Chinese Medicine (TCM) named entity recognition tasks—including difficulties in discovering domain-specific terms, inefficient dictionary construction, and insufficient recognition accuracy—this study proposes a domain-specific named entity recognition model based on domain knowledge graph enhancement and Lattice-LSTM. By applying embedding algorithms to a pre-constructed domain knowledge graph, we efficiently convert it into a domain-specific lexicon and incorporate multi-granularity lexical information through Lattice-LSTM to encode professional vocabulary in the dictionary into the model's input, thereby improving the model's effectiveness in domain-specific entity recognition tasks. Experiments on TCM datasets show that the F1-score of the proposed model is higher than that of traditional entity recognition models, verifying the model's validity.