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    政大機構典藏 > 資訊學院 > 資訊科學系 > 學位論文 >  Item 140.119/149645
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/149645


    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
    張瑜芸

    Liu, Jyi-Shane
    Chang, Yu-Yun

    黃鈺倫
    Huang, Yu-Lun
    Keywords: 深度學習
    多標籤分類
    線上醫療諮詢
    提問意圖
    語言行為
    統計分析
    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.
    Reference: Adams, C. (2002). Practitioner review: The assessment of language pragmatics. Journal of Child Psychology and Psychiatry, 43(8), 973–987.
    Ahangar, A. A., Sarani, A., & Dastuyi, S. Z. (2015). Apology speech act realization in sarawani balochi: A case study of male university students. Acta Scientiarum. Language and Culture, 37(2), 157–170.
    Akoglu, H. (2018). User’s guide to correlation coefficients. Turkish Journal of Emergency Medicine, 18(3), 91–93.
    Algotiml, B., Elmadany, A., & Magdy, W. (2019). Arabic tweet-act: Speech act recognition for arabic asynchronous conversations. Proceedings of the Fourth Arabic Natural Language Processing Workshop, 183–191.
    Amanullah, S., & Ramesh Shankar, R. (2020). The impact of covid-19 on physician burnout globally: A review. Healthcare, 8(4), 421.
    Aminifard, Y., Safaei, E., & Askari, H. (2014). Speech act of suggestion across language proficiency and gender in iranian context. International Journal of Applied Linguistics and English Literature, 3(5), 198–205.
    Archer, D., Culpeper, J., & Davies, M. (2008). Pragmatic annotation. Corpus Linguistics: An International Handbook, 1, 613–642.
    Austin, J. (1962). How to do things with words. Harvard University Press. Baeza-Yates, R., Calderón-Benavides, L., & González-Caro, C. (2006). The intention behind web queries. String Processing and Information Retrieval: 13th International Conference, SPIRE 2006, Glasgow, UK, October 11-13, 2006. Proceedings 13, 98–109.
    Baptista, S., Teixeira, A., Castro, L., Cunha, M., Serrão, C., Rodrigues, A., & Duarte, I. (2021). Physician burnout in primary care during the covid-19 pandemic: A cross-sectional study in portugal. Journal of Primary Care & Community Health, 12, 1–8.
    Bayat, B., Krauss, C., Merceron, A., & Arbanowski, S. (2016). Supervised speech act classification of messages in german online discussions. The Twenty-Ninth International Flairs Conference.
    Borji, A. (2023). A categorical archive of chatgpt failures. arXiv preprint arXiv:2302.03494.
    Broder, A. (2002). A taxonomy of web search. ACM Sigir forum, 36(2), 3–10.
    Broom, A. (2005). Virtually he@lthy: The impact of internet use on disease experience and the doctor-patient relationship. Qualitative Health Research, 15(3), 325–345.
    Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020). Language models are few- shot learners. Advances in Neural Information Processing Systems, 33, 1877–1901.
    Cai, R., Zhu, B., Ji, L., Hao, T., Yan, J., & Liu, W. (2017). An cnn-lstm attention approach to understanding user query intent from online health communities. 2017 IEEE International Conference on Data Mining Workshops (ICDMW), 430–437.
    Casanueva, I., Temčinas, T., Gerz, D., Henderson, M., & Vulić, I. (2020). Efficient intent detection with dual sentence encoders. arXiv preprint arXiv:2003.04807.
    Chatterjee, A., & Sengupta, S. (2020). Intent mining from past conversations for conversational agent. arXiv preprint arXiv:2005.11014.
    Chen, J., Lan, Y.-C., Chang, Y.-W., & Chang, P.-Y. (2020). Exploring doctors’ willingness to provide online counseling services: The roles of motivations and costs. International Journal of Environmental Research and Public Health, 17(1), 110.
    Chen, L., Chen, N., Zou, Y., Wang, Y., & Sun, X. (2022). A transformer-based threshold-free framework for multi-intent nlu. Proceedings of the 29th International Conference on Computational Linguistics, 7187–7192.
    Chen, N., Su, X., Liu, T., Hao, Q., & Wei, M. (2020). A benchmark dataset and case study for chinese medical question intent classification. BMC Medical Informatics and Decision Making, 20(3), 1–7.
    Chen, Q., Zhuo, Z., & Wang, W. (2019). Bert for joint intent classification and slot filling. arXiv preprint arXiv:1902.10909.
    Chiu, M.-H. P. (2016). An investigation of the questions posted on medical consultation websites. Health Information & Libraries Journal, 33(4), 283–294.
    Choi, E., & Shah, C. (2016). User motivations for asking questions in online q&a services. Journal of the Association for Information Science and Technology, 67(5), 1182–1197.
    Chou, W.-y. S., Prestin, A., Lyons, C., & Wen, K.-y. (2013). Web 2.0 for health pro- motion: Reviewing the current evidence. American Journal of Public Health, 103(1), e9–e18.
    Cohan, A., Ammar, W., Van Zuylen, M., & Cady, F. (2019). Structural scaffolds for citation intent classification in scientific publications. arXiv preprint arXiv:1904.01608.
    Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20, 37–46.
    Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd). Edward Arnold.
    Cohen, W., Carvalho, V., & Mitchell, T. (2004). Learning to classify email into “speech acts”. Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing, 309–316.
    Cramér, H. (1946). A contribution to the theory of statistical estimation. Scandinavian Actuarial Journal, 1946(1), 85–94.
    Cui, C., Mao, W., Zheng, X., & Zeng, D. (2017). Mining user intents in online interactions: Applying to discussions about medical event on sinaweibo platform. Smart Health: International Conference, ICSH 2017, Hong Kong, China, June 26-27, 2017, Proceedings, 177–183.
    De Witte, N. A., Carlbring, P., Etzelmueller, A., Nordgreen, T., Karekla, M., Haddouk, L., Belmont, A., Øverland, S., Abi-Habib, R., Bernaerts, S., et al. (2021). Online consultations in mental healthcare during the covid-19 outbreak: An international survey study on professionals’ motivations and perceived barriers. Inter-net Interventions, 25, 100405.
    Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.
    Dopierre, T., Gravier, C., Subercaze, J., & Logerais, W. (2020). Few-shot pseudo-labeling for intent detection. Proceedings of the 28th International Conference on Computational Linguistics, 4993–5003.
    Elmadany, A., Mubarak, H., & Magdy, W. (2018). Arsas: An arabic speech-act and sentiment corpus of tweets. OSACT, 3, 20.
    Elmadany, A., Abdou, S., & Gheith, M. (2018). Improving dialogue act classification for spontaneous arabic speech and instant messages at utterance level. arXiv preprint arXiv:1806.00522.
    Fleiss, J. L., Levin, B., & Paik, M. C. (2003). Statistical methods for rates and propositions (3rd). Wiley.
    Fu, H., & Wu, S. (2015). Determining the user intent of chinese-english mixed language queries based on search logs. iConference 2015 Proceedings.
    Gangadharaiah, R., & Narayanaswamy, B. (2019). Joint multiple intent detection and slot labeling for goal-oriented dialog. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), 564–569.
    Gilardi, F., Alizadeh, M., & Kubli, M. (2023). Chatgpt outperforms crowd-workers for text-annotation tasks. arXiv preprint arXiv:2303.15056.
    Girgis, L., Van Gurp, G., Zakus, D., & Andermann, A. (2018). Physician experiences and barriers to addressing the social determinants of health in the eastern mediterranean region: A qualitative research study. BMC Health Services Research, 18(1), 1–7.
    Gunawan, J. (2023). Exploring the future of nursing: Insights from the chatgpt model. Belitung Nursing Journal, 9(1), 1–5.
    Halliday, M. A. K. (1975). Learning how to mean: Explorations in the development of language. In Foundations of language development (pp. 239–265). Elsevier.
    Hashemian, M. (2021). A cross-cultural study of refusal speech act by persian l2 learners and american native speakers. Journal of Research in Applied Linguistics, 12(1), 81–98.
    Hu, J., Wang, G., Lochovsky, F., Sun, J.-t., & Chen, Z. (2009). Understanding user’s query intent with wikipedia. Proceedings of the 18th international conference on World wide web, 471–480.
    Hui, V. (2022). Exploring help-seeking behavior in online health communities among women with domestic violence experiences [Doctoral dissertation, University of Pittsburgh].
    Íñigo-Mora, I. (2004). On the use of the personal pronoun we in communities. Journal of Language and Politics, 3(1), 27–52.
    Irie, Y., Matsubara, S., Kawaguchi, N., Yamaguchi, Y., & Inagaki, Y. (2006). Layered speech-act annotation for spoken dialogue corpus. LREC, 1584–1589.
    Jansen, B. J., & Booth, D. (2010). Classifying web queries by topic and user intent. CHI’10 Extended Abstracts on Human Factors in Computing Systems, 4285–4290.
    Jenset, G. B. (2008). Basic statistics for corpus linguistics. Handout for methods seminar in English linguistics, 1–20.
    Jeong, M., Lin, C.-Y., & Lee, G. G. (2009). Semi-supervised speech act recognition in emails and forums. Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, 1250–1259.
    Jiao, W., Wang, W., Huang, J.-t., Wang, X., & Tu, Z. (2023). Is chatgpt a good translator? a preliminary study. arXiv preprint arXiv:2301.08745.
    Jimmy, J., Zuccon, G., Palotti, J., Goeuriot, L., & Kelly, L. (2018). Overview of the clef 2018 consumer health search task. CLEF 2018 Working Notes, 2125.
    Joinson, A. N. (2001). Self-disclosure in computer-mediated communication: The role of self-awareness and visual anonymity. European Journal of Social Psychology, 31(2), 177–192.
    Joty, S., & Hoque, E. (2016). Speech act modeling of written asynchronous conversations with task-specific embeddings and conditional structured models. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 1746–1756.
    Joukhadar, A., Ghneim, N., & Rebdawi, G. (2021). Impact of using bidirectional encoder representations from transformers (bert) models for arabic dialogue acts identification. Ingénierie des Systèmes d Inf., 26(5), 469–475.
    Kachru, Y. (1998). Culture and speech acts: Evidence from indian and singaporean english. Studies in the Linguistic Sciences, 28(1), 79–98.
    Kalla, D., & Smith, N. (2023). Study and analysis of chat gpt and its impact on differ- ent fields of study. International Journal of Innovative Science and Research Technology, 8(3).
    Kang, S., Kim, H., & Seo, J. (2010). A reliable multidomain model for speech act classification Pattern Recognition Letters, 31(1), 71–74.
    Kang, S., Ko, Y., & Seo, J. (2013). Hierarchical speech-act classification for discourse analysis. Pattern Recognition Letters, 34(10), 1119–1124.
    Khanpour, H., Guntakandla, N., & Nielsen, R. (2016). Dialogue act classification in domain-independent conversations using a deep recurrent neural network. Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, 2012–2021.
    Kim, H.-S., Seon, C.-N., & Seo, J.-Y. (2011). Review of korean speech act classifica- tion: Machine learning methods. Journal of Computing Science and Engineering, 5(4), 288–293.
    Kim, Y., Guo, L., Yu, B., & Li, Y. (2023). Can chatgpt understand causal language in science claims? Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis, 379–389.
    Ko, Y. (2015). New feature weighting approaches for speech-act classification. Pattern Recognition Letters, 51, 107–111.
    Kohandani, M., Farzaneh, N., & Kazemi, M. (2014). A critical analysis of speech acts and language functions in top notch series. Procedia-Social and Behavioral Sciences, 98, 1009–1015.
    Lampert, A., Dale, R., & Paris, C. (2006). Classifying speech acts using verbal response modes. Proceedings of the Australasian Language Technology Workshop 2006, 34–41.
    Larson, S., Mahendran, A., Peper, J. J., Clarke, C., Lee, A., Hill, P., Kummerfeld, J. K., Leach, K., Laurenzano, M. A., Tang, L., et al. (2019). An evaluation dataset for intent classification and out-of-scope prediction. arXiv preprint arXiv:1909.02027.
    Laurenti, E., Bourgon, N., Benamara, F., Mari, A., Moriceau, V., & Courgeon, C. (2022). Give me your intentions, i’ll predict our actions: A two-level classification of speech acts for crisis management in social media. 13th Conference on Language Resources and Evaluation (LREC 2022), 4333–4343.
    Lebanoff, L., Newton, C., Hung, V., Atkinson, B., Killilea, J., & Liu, F. (2021). Semantic parsing of brief and multi-intent natural language utterances. Proceedings of the Second Workshop on Domain Adaptation for NLP, 255–262.
    Lee, J.-w., & Kim, G. C. (1997). A dialogue analysis model with statistical speech act processing for dialogue machine translation. Spoken Language Translation.
    Lester, B., Choudhury, S. R., Prasad, R., & Bangalore, S. (2021). Intent features for rich natural language understanding. arXiv preprint arXiv:2104.08701.
    Levin, L., Langley, C., Lavie, A., Gates, D., Wallace, D., & Peterson, K. (2003). Domain specific speech acts for spoken language translation. Proceedings of the Fourth SIGdial Workshop of Discourse and Dialogue, 208–217.
    Li, W., & Wu, Y. (2019). Multi-level gated recurrent neural network for dialog act classification arXiv preprint arXiv:1910.01822.
    Liu, B., & Lane, I. (2016). Attention-based recurrent neural network models for joint intent detection and slot filling. arXiv preprint arXiv:1609.01454.
    Liu, H., Zhang, X., Fan, L., Fu, X., Li, Q., Wu, X.-M., & Lam, A. Y. (2019). Re-constructing capsule networks for zero-shot intent classification. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), 4799–4809.
    Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2023). Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing ACM Computing Surveys, 55(9), 1–35.
    Liu, X., Eshghi, A., Swietojanski, P., & Rieser, V. (2019). Benchmarking natural language understanding services for building conversational agents. arXiv preprint arXiv:1903.05566.
    Liu, Z., & Jansen, B. J. (2015). Subjective versus objective questions: Perception of question subjectivity in social q&a. Social Computing, Behavioral-Cultural Modeling, and Prediction: 8th International Conference, SBP 2015, Washington, DC, USA, March 31-April 3, 2015. Proceedings 8, 131–140.
    Lu, J., Sridhar, S., Pandey, R., Hasan, M. A., & Mohler, G. (2019). Investigate translations into drug addiction through text mining of reddit data. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2367–2375.
    Mao, Y., & Zhao, X. (2019). I am a doctor, and here is my proof: Chinese doctors’ identity constructed on the online medical consultation websites. Health Communication, 34(13), 1645–1652.
    Mao, Y., & Zhao, X. (2020). By the mitigation one knows the doctor: Mitigation strategies by chinese doctors in online medical consultation. Health Communication, 35(6), 667–674.
    Martikainen, S., Falcon, M., Wikström, V., Peltola, S., & Saarikivi, K. (2022). Perceptions of doctors’ empathy and patients’ subjective health status at an online clinic: Development of an empathic anamnesis questionnaire. Psychosomatic Medicine, 84(4), 513.
    Martínez, N. D. C. (2011). Cognitive modeling in illocutionary meaning. Review of Cognitive Linguistics, 9(2), 392–412.
    Mathew, K. V., Tarigoppula, V. S. A., & Frermann, L. (2021). Multi-modal intent classification for assistive robots with large-scale naturalistic datasets. Proceedings of the 19th Annual Workshop of the Australasian Language Technology Association, 47–57.
    Mauni, H. Z., Hossain, T., & Rab, R. (2020). Classification of underrepresented text data in an imbalanced dataset using deep neural network. 2020 IEEE Region 10 Symposium (TENSYMP), 997–1000.
    McHugh, M. L. (2013). The chi-square test of independence. Biochemia Medica, 23(2), 143–149.
    McRoy, S. W., & Hirst, G. (1995). The repair of speech act misunderstandings by abductive inference. Computational Linguistics, 21(4), 435–478.
    Meyer, D., Zeileis, A., & Hornik, K. (2023). Vcd: Visualizing categorical data [R package version 1.4-11]. https://CRAN.R-project.org/package=vcd
    Mezzi, R., Yahyaoui, A., Krir, M. W., Boulila, W., & Koubaa, A. (2022). Mental health intent recognition for arabic-speaking patients using the mini international neuropsychiatric interview (mini) and bert model. Sensors, 22(3).
    Mohiuddin, T., Nguyen, T.-T., & Joty, S. (2019). Adaptation of hierarchical structured models for speech act recognition in asynchronous conversation. arXiv preprint arXiv:1904.04021.
    Moldovan, C., Rus, V., & Graesser, A. C. (2011). Automated speech act classification for online chat. MAICS, 710, 23–29.
    Morady Moghaddam, M. (2013). Discourse structures of condolence speech act. Two Quarterly Journal of English Language Teaching and Learning University of Tabriz, 4(10), 105–125.
    Nahl, D., & Bilal, D. (2007). Information and emotion: The emergent affective paradigm in information behavior research and theory. Information Today, Inc.
    Nakamura, K. (2005). Appreciation strategies of german and japanese native speakers and german learners of japanese. Proceedings of the 4th Annual JALT Pan-SIG Conference, 14–15.
    Nambisan, P. (2011). Information seeking and social support in online health communities: Impact on patients’ perceived empathy. Journal of the American Medical Informatics Association, 18(3), 298–304.
    Negargar, S. (2015). A contrastive study of speech acts of greeting in two persian and english soap operas with regard to the level of formality, structure and frequency. IMPACT: International Journal of Research in Humanities, Arts and Literature, 3(6), 47–60.
    Niu, P., Chen, Z., Song, M., et al. (2019). A novel bi-directional interrelated model for joint intent detection and slot filling. arXiv preprint arXiv:1907.00390.
    Nourdad, N., Mohammadnia, Z., & Khiabani, L. R. (2016). Evaluation of speech acts in the newly developed iranian efl english textbooks. Modern Journal of Language Teaching Methods (MJLTM), 4(2), 1–10.
    OpenAI. (2023). Gpt-4 technical report.
    O’Shea, J., Bandar, Z., & Crockett, K. (2010). A machine learning approach to speech
    act classification using function words. Agent and Multi-Agent Systems: Technologies and Applications: 4th KES International Symposium, KES-AMSTA 2010, Gdynia, Poland, June 23-25, 2010, Proceedings. Part II 4, 82–91.
    Pearson, K. (1900). X. on the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 50(302), 157–175.
    Pinhanez, C. S., Candello, H., Pichiliani, M. C., Vasconcelos, M., Guerra, M., de Bayser, M. G., & Cavalin, P. (2018). Different but equal: Comparing user collaboration with digital personal assistants vs. teams of expert agents. arXiv preprint arXiv:1808.08157.
    Plakidis, M., & Rehm, G. (2022). A dataset of offensive german language tweets annotated for speech acts. Proceedings of the Thirteenth Language Resources and Evaluation Conference, 4799–4807.
    Poupari, Z., & Bagheri, M. S. (2013). Correlation of speech acts and language functions in top notch series vs. ili textbooks from a pragmatic point of view. International Journal of English Linguistics, 3(2), 72.
    Purohit, H., Dong, G., Shalin, V., Thirunarayan, K., & Sheth, A. (2015). Intent classification of short-text on social media. 2015 IEEE international conference on Smart City/SocialCom/SustainCom, 222–228.
    Qadir, A., & Riloff, E. (2011). Classifying sentences as speech acts in message board posts. Proceedings of the 2011 Conference on Empirical Methods In Natural Language Processing, 748–758.
    Qin, L., Che, W., Li, Y., Wen, H., & Liu, T. (2019). A stack-propagation framework with token-level intent detection for spoken language understanding. arXiv preprint arXiv:1909.02188.
    R Core Team. (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria. https://www.R-project.org/ Radford, A., Narasimhan, K., Salimans, T., Sutskever, I., et al. (2018). Improving language understanding by generative pre-training.
    Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al. (2019). Language models are unsupervised multitask learners. OpenAI Blog, 1(8), 9. Raheja, V., & Tetreault, J. (2019). Dialogue act classification with context-aware self-attention. arXiv preprint arXiv:1904.02594.
    Rasor, T., Olney, A., & D’Mello, S. (2011). Student speech act classification using machine learning. Twenty-Fourth International FLAIRS Conference.
    Ravuri, S., & Stoicke, A. (2015). A comparative study of neural network models for lexical intent classification. 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), 368–374.
    Ravuri, S., & Stolcke, A. (2015). Recurrent neural network and lstm models for lexical utterance classification. 16th Annual Conference of the International Speech Communication Association.
    Ries, K. (1999). Hmm and neural network based speech act detection. 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No. 99CH36258), 1, 497–500.
    Rojowiec, R., Roth, B., & Fink, M. (2020). Intent recognition in doctor-patient interviews. Proceedings of the Twelfth Language Resources and Evaluation Confer- ence, 702–709.
    Roman, M., Shahid, A., Khan, S., Koubaa, A., & Yu, L. (2021). Citation intent classification using word embedding. IEEE Access, 9, 9982–9995.
    Rosenbaum, A., Soltan, S., Hamza, W., Versley, Y., & Boese, M. (2022). Linguist: Language model instruction tuning to generate annotated utterances for intent clas- sification and slot tagging. arXiv preprint arXiv:2209.09900.
    Sadohara, K., Kojima, H., Narita, T., Nihei, M., Kamata, M., Onaka, S., Fujita, Y., & Inoue, T. (2013). Sub-lexical dialogue act classification in a spoken dialogue system support for the elderly with cognitive disabilities. Proceedings of the Fourth Workshop on Speech and Language Processing for Assistive Technologies, 93– 98.
    Saha, T., Jayashree, S. R., Saha, S., & Bhattacharyya, P. (2020). Bert-caps: A transformer-based capsule network for tweet act classification. IEEE Transactions on Computational Social Systems, 7(5), 1168–1179.
    Saha, T., Saha, S., & Bhattacharyya, P. (2019). Tweet act classification: A deep learning based classifier for recognizing speech acts in twitter. 2019 International Joint Conference on Neural Networks (IJCNN), 1–8.
    Saha, T., Upadhyaya, A., Saha, S., & Bhattacharyya, P. (2021). Towards sentiment and emotion aided multi-modal speech act classification in twitter. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 5727–5737.
    Sahu, G., Rodriguez, P., Laradji, I. H., Atighehchian, P., Vazquez, D., & Bahdanau, D. (2022). Data augmentation for intent classification with off-the-shelf large language models. arXiv preprint arXiv:2204.01959.
    Sallam, M. (2023). Chatgpt utility in healthcare education, research, and practice: Systematic review on the promising perspectives and valid concerns. Healthcare, 11(6), 887.
    Schuurmans, J., & Frasincar, F. (2019). Intent classification for dialogue utterances. IEEE Intelligent Systems, 35(1), 82–88.
    Searle, J. R. (1975). Indirect speech acts. In P. Cole & J. Morgan (Eds.), Syntax and semantics 3: Speech acts (pp. 59–82). Academic Press.
    Searle, J. R. (1976). A classification of illocutionary acts. Language in Society, 5(1), 1–23.
    Shah, S., Calderon, M. D., Soin, A., Manchikanti, L., et al. (2020). The effect of covid-19 on interventional pain management practices: A physician burnout survey. Pain Physician, 23(4S), S271.
    Shala, L., Rus, V., & Graesser, A. C. (2010). Automated speech act classification in arabic. Subjetividad y Procesos Cognitivos, 14, 284–292.
    Shams, R., & Afghari, A. (2011). Effects of culture and gender in comprehension of speech acts of indirect request. English Language Teaching, 4(4), 279–287.
    Shang, G., Tixier, A. J.-P., Vazirgiannis, M., & Lorré, J.-P. (2020). Speaker-change aware crf for dialogue act classification. arXiv preprint arXiv:2004.02913.
    Shen, A. C.-T. (2011). Cultural barriers to help-seeking among taiwanese female victims of dating violence. Journal of Interpersonal Violence, 26(7), 1343–1365.
    Singhal, K., Tu, T., Gottweis, J., Sayres, R., Wulczyn, E., Hou, L., Clark, K., Pfohl, S., Cole-Lewis, H., Neal, D., et al. (2023). Towards expert-level medical question answering with large language models. arXiv preprint arXiv:2305.09617.
    Smith, L. N. (2017). Cyclical learning rates for training neural networks. 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), 464–472.
    Soldaini, L., Yates, A., Yom-Tov, E., Frieder, O., & Goharian, N. (2016). Enhancing web search in the medical domain via query clarification. Information Retrieval Journal, 19, 149–173.
    Soleimani, H., & Yeganeh, M. N. (2016). An analysis of pragmatic competence in 2013 presidential election candidates of iran: A comparison of speech acts with the poll outcomes. Theory and Practice in Language Studies, 6(4), 706.
    Stolcke, A., Ries, K., Coccaro, N., Shriberg, E., Bates, R., Jurafsky, D., Taylor, P., Martin, R., Ess-Dykema, C. V., & Meteer, M. (2000). Dialogue act modeling for automatic tagging and recognition of conversational speech. Computational Linguistics, 26(3), 339–373.
    Subramanian, S., Cohn, T., & Baldwin, T. (2019). Target based speech act classification in political campaign text. arXiv preprint arXiv:1905.07856.
    Sun, S., Xie, Z., Yu, K., Jiang, B., Zheng, S., & Pan, X. (2021). Covid-19 and healthcare system in china: Challenges and progression for a sustainable future. Globalization and Health, 17(1), 1–8.
    Sun, S., Pan, W., & Wang, L. L. (2010). A comprehensive review of effect size reporting and interpreting practices in academic journals in education and psychology. Journal of Educational Psychology, 102(4), 989.
    Tanaka, H., & Yokoo, A. (1999). An efficient statistical speech act type tagging system for speech translation systems. Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics, 381–388.
    Tang, T., Tang, X., & Yuan, T. (2020). Fine-tuning bert for multi-label sentiment analysis in unbalanced code-switching text. IEEE Access, 8, 193248–193256.
    Tangcharoensathien, V., Calleja, N., Nguyen, T., Purnat, T., D’Agostino, M., Garcia-Saiso, S., Landry, M., Rashidian, A., Hamilton, C., AbdAllah, A., et al. (2020). Framework for managing the covid-19 infodemic: Methods and results of an online, crowdsourced who technical consultation. Journal of Medical Internet Research, 22(6), e19659.
    Tohti, T., Abdurxit, M., & Hamdulla, A. (2022). Medical qa oriented multi-task learning model for question intent classification and named entity recognition. Information, 13(12), 581.
    Tran, Q. H., Zukerman, I., & Haffari, G. (2017). Preserving distributional information in dialogue act classification. Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, 2151–2156.
    Tseng, M.-Y. (2021). Toward a cognitive-pragmatic account of metonymic schemes of thought: Examples from online medical consultation. Journal of Pragmatics, 173, 177–188.
    Tseng, M.-Y., & Zhang, G. (2018). Pragmeme, adaptability, and elasticity in online medical consultations. Journal of Pragmatics, 137, 40–56.
    Tseng, M.-Y., & Zhang, G. (2020). Perceptions of and attitudes toward elastic language in online health communication in chinese. Lingua, 233, 102750.
    Tseng, M.-Y., & Zhang, G. (2022). Conceptual metonymy and emotive-affective meaning at the interface: Examples from online medical consultations. Lingua, 268, 103192.
    Uludag, K. (2023). Testing creativity of chatgpt in psychology: Interview with chatgpt. Available at SSRN 4390872.
    Vaezi, R., Tabatabaei, S., & Bakhtiarvand, M. (2014). A comparative study of speech acts in the textbooks by native and non-native speakers: A pragmatic analysis of new interchange series vs. locally-made efl textbooks. Theory and Practice in Language Studies, 4(1), 167.
    van Rijsbergen, C. J. (1975). Information retrieval. Butterworth.
    Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.
    Vosoughi, S., & Roy, D. (2016). Tweet acts: A speech act classifier for twitter. Proceedings of the International AAAI Conference on Web and Social Media, 10(1), 711–714.
    Wang, A., Singh, A., Michael, J., Hill, F., Levy, O., & Bowman, S. R. (2018). Glue: A multi-task benchmark and analysis platform for natural language understanding. arXiv preprint arXiv:1804.07461.
    Wang, J., Liang, Y., Meng, F., Li, Z., Qu, J., & Zhou, J. (2023). Cross-lingual summarization via chatgpt. arXiv preprint arXiv:2302.14229.
    Wang, Y., Wang, S., Li, Y., & Dou, D. (2022). Recognizing medical search query intent by few-shot learning. Proceedings of the 45th International ACM SIGIR
    Conference on Research and Development in Information Retrieval, 502–512. Wang, Y., Shen, Y., & Jin, H. (2018). A bi-model based rnn semantic frame parsing
    model for intent detection and slot filling. arXiv preprint arXiv:1812.10235.
    Webb, N., & Liu, T. (2008). Investigating the portability of corpus-derived cue phrases for dialogue act classification. Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008), 977–984.
    Wei, H. (2012). Cross-cultural comparisons of english request speech acts in native speakers of english and chinese. Cross-Cultural Communication, 8(4), 24.
    White, M., & Dorman, S. M. (2001). Receiving social support online: Implications for health education. Health Education Research, 16(6), 693–707.
    Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., et al. (2020). Transformers: State-of-the-art natural language processing. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, 38–45. https://doi. org/10.18653/v1/2020.emnlp-demos.6
    Wu, C., Luo, G., Guo, C., Ren, Y., Zheng, A., & Yang, C. (2020). An attention-based multi-task model for named entity recognition and intent analysis of chinese online medical questions. Journal of Biomedical Informatics, 108, 103511.
    Wu, T.-W., Su, R., & Juang, B. (2021). A label-aware bert attention network for zero- shot multi-intent detection in spoken language understanding. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 4884–4896.
    Wu, X., Duan, R., & Ni, J. (2023). Unveiling security, privacy, and ethical concerns of chatgpt. arXiv preprint arXiv:2307.14192.
    Wu, Y., Wang, H., Zhang, D., Chen, G., & Zhang, H. (2022). Incorporating instructional prompts into a unified generative framework for joint multiple intent detection and slot filling. Proceedings of the 29th International Conference on Computational Linguistics, 7203–7208.
    Xiang, Y.-T., Zhao, N., Zhao, Y.-J., Liu, Z., Zhang, Q., Feng, Y., Yan, X.-N., Cheung, T., & Ng, C. H. (2020). An overview of the expert consensus on the mental health treatment and services for major psychiatric disorders during covid-19 outbreak: China’s experiences. International Journal of Biological Sciences, 16(13), 2265.
    Xu, G., Lee, H., Koo, M.-W., & Seo, J. (2017). Convolutional neural network using a threshold predictor for multi-label speech act classification. 2017 IEEE International Conference on Big Data and Smart Computing (BigComp), 126–130.
    Xu, H., & Huang, C.-R. (2014). Annotate and identify modalities, speech acts and finer-grained event types in chinese text. Proceedings of Workshop on Lexical and Grammatical Resources for Language Processing, 157–166.
    Xu, J., Zhang, Q., & Huang, X. (2013). Understanding the semantic intent of domain-specific natural language query. Proceeding of International Joint Conference on Natural Language Processing, 552–560.
    Xu, P., & Sarikaya, R. (2013). Convolutional neural network based triangular crf for joint intent detection and slot filling. 2013 IEEE Workshop on Automatic Speech Recognition and Understanding, 78–83.
    Yan, L., & Tan, Y. (2014). Feeling blue? go online: An empirical study of social support among patients. Information Systems Research, 25(4), 690–709.
    Yoo, D., Ko, Y., & Seo, J. (2017). Speech-act classification using a convolutional neural network based on pos tag and dependency-relation bigram embedding. IEICE Transactions on Information and Systems, 100(12), 3081–3084.
    Yoosefvand, A., & Rasekh, A. E. (2014). A comparative study of gratitude speech act between persian and english speakers. Journal of Applied Linguistics and Language Research, 1(2), 44–61.
    You, Y., Li, J., Reddi, S., Hseu, J., Kumar, S., Bhojanapalli, S., Song, X., Demmel, J., Keutzer, K., & Hsieh, C.-J. (2019). Large batch optimization for deep learning: Training bert in 76 minutes. arXiv preprint arXiv:1904.00962.
    Zhang, C., Du, N., Fan, W., Li, Y., Lu, C.-T., & Philip, S. Y. (2017). Bringing semantic structures to user intent detection in online medical queries. 2017 IEEE International Conference on Big Data (Big Data), 1019–1026.
    Zhang, C., Fan, W., Du, N., & Yu, P. S. (2016). Mining user intentions from medical queries: A neural network based heterogeneous jointly modeling approach. Proceedings of the 25th International Conference on World wide web, 1373–1384.
    Zhang, H., Liang, H., Zhang, Y., Zhan, L., Wu, X.-M., Lu, X., & Lam, A. (2022). Fine-tuning pre-trained language models for few-shot intent detection: Supervised pre-training and isotropization. arXiv preprint arXiv:2205.07208.
    Zhang, H., Zhang, Y., Zhan, L.-M., Chen, J., Shi, G., Wu, X.-M., & Lam, A. (2021). Effectiveness of pre-training for few-shot intent classification. arXiv preprint arXiv:2109.05782.
    Zhang, R., Gao, D., & Li, W. (2012). Towards scalable speech act recognition in twitter: Tackling insufficient training data. Proceedings of the Workshop on Semantic Analysis in Social Media, 18–27.
    Zhang, T., Cho, J. H., & Zhai, C. (2014). Understanding user intents in online health forums. Proceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, 220–229.
    Zhang, Y. (2010). Contextualizing consumer health information searching: An analysis of questions in a social q&a community. Proceedings of the 1st ACM International Health Informatics Symposium, 210–219.
    Zhang, Y. (2013). Toward a layered model of context for health information searching: An analysis of consumer-generated questions. Journal of the American Society for Information Science and Technology, 64(6), 1158–1172.
    Zhang, Y. (2021). How doctors do things with empathy in online medical consultations in china: A discourse-analytic approach. Health Communication, 36(7), 816– 825.
    Zhao, X., & Mao, Y. (2021). Trust me, i am a doctor: Discourse of trustworthiness by chinese doctors in online medical consultation. Health Communication, 36(3), 372–380.
    Zhong, Q., Ding, L., Liu, J., Du, B., & Tao, D. (2023). Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198.
    Zhu, W., Ni, Y., Wang, X., & Xie, G. (2021). Discovering better model architectures for medical query understanding. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers, 230–237.
    Ziems, C., Held, W., Shaikh, O., Chen, J., Zhang, Z., & Yang, D. (2023). Can large language models transform computational social science? arXiv preprint arXiv:2305.03514.
    Zimmermann, C., Del Piccolo, L., Bensing, J., Bergvik, S., De Haes, H., Eide, H., Fletcher, I., Goss, C., Heaven, C., Humphris, G., et al. (2011). Coding patient emotional cues and concerns in medical consultations: The verona coding definitions of emotional sequences (vr-codes). Patient Education and Counseling, 82(2), 141–148.
    Zummo, M. L. (2015). Exploring web-mediated communication: A genre-based linguistic study for new patterns of doctor-patient interaction in online environment. Communication & Medicine, 12(2-3), 187.
    Description: 碩士
    國立政治大學
    資訊科學系
    110753134
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