The TMUNLPG2 team participated in the Japanese subtask of the NTCIR-18 Medical Natural Language Processing for AI Chat (MedNLP-CHAT) Task. This paper presents our methodological approach and analyzes the official results. For the Japanese subtask, we implemented two distinct methodologies addressing the objective and subjective components. In the objective task, we fine-tuned a pre-trained language model enhanced with focal loss, comprehensive feature engineering, and strategic data augmentation techniques to optimize performance. For the subjective task, we developed specialized feature engineering methods to extract implicit semantic relationships within question-answer pairs, subsequently leveraging these features to train a robust deep learning architecture. Our approach yielded significant results, with TMUNLPG2 achieving the highest average F1-score among seven participating teams in the objective task and securing second place in the subjective task. These outcomes demonstrate the efficacy of our methodological framework and highlight its potential applications in advancing medical natural language processing systems.
Yang et al. (Fri,) studied this question.
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