Part-of-Speech Tagging for Traditional Chinese Medicine Clinical Pulse Feeling and Palpation Information Based on Hidden Markov Model Prompt-Tuning

Abstract

 This paper proposes a Traditional Chinese Medicine Clinical Pulse Feeling and Palpation Information part-of-speech tagging method based on Hidden Markov Model Prompt-Tuning, aimed at addressing the challenges of scarce annotated samples and the issue of words with multiple parts of speech in Traditional Chinese Medicine Clinical Pulse Feeling and Palpation Information part-of-speech tagging tasks. By designing specific prompt templates, this method transforms the downstream task into a cloze task for the pretrained language model during its pretraining phase, thereby bridging the gap between pretraining and fine-tuning objectives and enhancing the knowledge representation ability of the pretrained language model for downstream tasks. This approach also integrates the part-of-speech information of adjacent words into the prompt templates and uses the Hidden Markov Model to model the dependencies between adjacent words' parts of speech, effectively improving the overall performance of the model in the Traditional Chinese Medicine Clinical Pulse Feeling and Palpation Information part-of-speech tagging task, particularly in identifying words with multiple parts of speech. Furthermore, empirical evidence demonstrates the outstanding performance of the proposed method in this part-of-speech tagging task, achieving an F1 score of up to 85.52%.