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    Title: 以深度學習模型自動分類線上醫療提問意圖與回覆語言行為
    Automatic classification of inquisitive query intent and responsive speech acts in online medical consultation using deep learning models
    Authors: 黃鈺倫
    Huang, Yu-Lun
    Contributors: 劉吉軒

    Liu, Jyi-Shane
    Chang, Yu-Yun

    Huang, Yu-Lun
    Keywords: 深度學習
    deep learning
    multi-label classification
    online medical consultation
    query intent
    speech acts
    statistical analysis
    Date: 2024
    Issue Date: 2024-02-01 11:40:25 (UTC+8)
    Abstract: 本研究將語言學知識融入線上醫療諮詢中提問意圖和回覆言語行為的分類框架,並探討它們之間的統計關係,以檢視哪些組合能讓提問者滿意醫師回覆。本研究採多標籤方式標記醫療問答,因為一則提問可能包含多個意圖,而一則回覆也可能涵蓋多個語言行為。研究流程首先以卡方檢定和克雷莫V係數檢驗標記後語料庫中,提問意圖和回覆言語行為間的統計關係。統計分析揭示情感意圖和心理語言行為之間存在高度統計顯著性,意味著醫師在回應提問者的情感意圖時,若運用心理語言行為,將能達到令提問者滿意的效果。隨後,本研究將深度學習模型應用於標記後的語料庫,以分類提問意圖和回覆言語行為,分類結果顯示GPT-3.5的模型表現相對優於BERT,顯示GPT-3.5有望作為線上健康支持系統,協助醫師辨識提問意圖並根據統計相關性提供相應的語言行為。此外,偏誤分析顯示,分類錯誤之成因可能為訓練資料筆數和語言線索,包括高頻詞、詞彙歧義、多字詞語、否定結構、假設結構、名詞--動詞分離結構、語言間接性,和線上醫療諮詢語境賦使。
    This study integrates linguistic knowledge into the classification scheme for query intent and speech acts in online medical consultation (OMC), assessing their statistical relationship to examine which combinations can achieve inquirer satisfaction with physician responses. Medical queries and responses were annotated with multiple labels, as a query may convey multiple intentions and a response may perform multiple speech acts. The annotated OMC corpus first underwent statistical analysis using the chi-square and Cramér's V tests. The statistical analysis reveals a strong correlation between emotional intent and psychological acts, suggesting that doctors' use of psychological acts can address inquirers' emotional intent and thereby gain inquirer satisfaction. Subsequently, the OMC corpus was applied to the classification of query intent and speech acts using deep learning models. The classification results show GPT-3.5's relatively better performance over BERT, implying that GPT-3.5 can serve as an online health support system to assist doctors in identifying query intents and suggesting appropriate corresponding speech acts based on statistical correlations. Moreover, the error analysis suggests that misclassification may stem from training data quantity and linguistic cues, including strong linguistic cues, ambiguity, multi-word expressions, negation, hypothetical constructions, noun-verb separation, discursive indirectness, and OMC affordances.
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