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<p>In medical retrieval scenarios, accurate identification of user query intent is pivotal for enhancing retrieval relevance and knowledge augmentation effectiveness. However, existing approaches exhibit polarized performance: traditional shallow models (e.g., TF-IDF, Bag-of-Words) struggle to capture contextual semantics, while pre-trained language models, despite their strong deep semantic representation capabilities, often overlook keyword weighting and category-specific priors. This leads to suboptimal accuracy for long-tail categories and susceptibility to confusion from emotional or colloquial query formulations. To address these challenges, this study proposes a hybrid model integrating RoBERTa-wwm-ext with sentence-level TF-IDF and category-centroid TF-IDF via a three-branch attention fusion mechanism. The model employs adaptive attention mechanisms to dynamically weight deep-shallow features at the sample level, while incorporating category-centroid vectors as explicit semantic anchors to mitigate class imbalance issues. Experimental evaluations on the KUAKE-QIC dataset demonstrate that our approach achieves an accuracy of 0.824 and a Macro-F1 score of 0.800, outperforming all baseline models by 2.3% points. These results highlight the synergistic benefits of deep-shallow feature interaction, ensuring robust recall for dominant categories while significantly improving precision for minority classes. This work provides a generalizable framework for medical intent classification that balances semantic integrity with keyword-driven priors, laying a more reliable foundation for applications such as clinical question-answering systems, medical search ranking, and intelligent triage systems.</p>

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A hybrid model integrating RoBERTa, TF-IDF, and attention mechanism for medical query intent classification

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月28日

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  • Zejian Liang,
  • Yunxiang Zhao,
  • Haiwen Xu,
  • Hong Huang,
  • Luning Chen

摘要

In medical retrieval scenarios, accurate identification of user query intent is pivotal for enhancing retrieval relevance and knowledge augmentation effectiveness. However, existing approaches exhibit polarized performance: traditional shallow models (e.g., TF-IDF, Bag-of-Words) struggle to capture contextual semantics, while pre-trained language models, despite their strong deep semantic representation capabilities, often overlook keyword weighting and category-specific priors. This leads to suboptimal accuracy for long-tail categories and susceptibility to confusion from emotional or colloquial query formulations. To address these challenges, this study proposes a hybrid model integrating RoBERTa-wwm-ext with sentence-level TF-IDF and category-centroid TF-IDF via a three-branch attention fusion mechanism. The model employs adaptive attention mechanisms to dynamically weight deep-shallow features at the sample level, while incorporating category-centroid vectors as explicit semantic anchors to mitigate class imbalance issues. Experimental evaluations on the KUAKE-QIC dataset demonstrate that our approach achieves an accuracy of 0.824 and a Macro-F1 score of 0.800, outperforming all baseline models by 2.3% points. These results highlight the synergistic benefits of deep-shallow feature interaction, ensuring robust recall for dominant categories while significantly improving precision for minority classes. This work provides a generalizable framework for medical intent classification that balances semantic integrity with keyword-driven priors, laying a more reliable foundation for applications such as clinical question-answering systems, medical search ranking, and intelligent triage systems.