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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 張瑞益 | zh_TW |
| dc.contributor.advisor | Ray-I Chang | en |
| dc.contributor.author | 朱健 | zh_TW |
| dc.contributor.author | JIAN ZHU | en |
| dc.date.accessioned | 2023-05-05T17:30:41Z | - |
| dc.date.available | 2023-11-09 | - |
| dc.date.copyright | 2023-05-05 | - |
| dc.date.issued | 2023 | - |
| dc.date.submitted | 2023-02-15 | - |
| dc.identifier.citation | K. Daisaku, I. Kaoru, and W. Shoko, “Detecting early stage dementia based on natural language processing,” Jpn. Soc. Artif. Intell, vol. 34, no. 4, pp. 1-7, 2019.
L. Tóth et al., “A speech recognition-based solution for the automatic detection of mild cognitive impairment from spontaneous speech,” Current Alzheimer Research, vol. 15, no. 2, pp. 130-138, 2018. C. Baker et al., "Automatic multi-class non-functional software requirements classification using neural networks." pp. 610-615. K. C. Fraser, J. A. Meltzer, and F. Rudzicz, “Linguistic features identify Alzheimer’s disease in narrative speech,” Journal of Alzheimer's Disease, vol. 49, no. 2, pp. 407-422, 2016. S. Karlekar, T. Niu, and M. Bansal, “Detecting linguistic characteristics of Alzheimer's dementia by interpreting neural models,” arXiv preprint arXiv:1804.06440, 2018. E. Mossello, and D. Simoni, “High blood pressure in older subjects with cognitive impairment,” Monaldi Archives for Chest Disease, vol. 84, no. 1-2, 2015. S. Borson et al., “Borson S, Scanlan J, Brush M, et al. The mini-cog: A cognitive 'vital signs' measure for dementia screening in multi-lingual elderly,” Int J Geriatr Psychiatry, vol. 15, no. 11, pp. 1021-1027, 2000. U. Madhow, and M. L. Honig, “MMSE interference suppression for direct-sequence spread-spectrum CDMA,” IEEE Trans Commun, vol. 42, no. 12, pp. 3178-3188, 1994. M. F. Folstein, S. E. Folstein, and P. R. McHugh, ““Mini-mental state”: a practical method for grading the cognitive state of patients for the clinician,” Journal of psychiatric research, vol. 12, no. 3, pp. 189-198, 1975. 林佩欣. "認知功能評估," https://www.tpta.org.tw/images/news/20180829021240-0934988A-B298-26/PT_LEVEL%20II_%E6%9E%97%E4%BD%A9%E6%AC%A3%E8%80%81%E5%B8%AB_6%20%E8%AA%8D%E7%9F%A5%E5%8A%9F%E8%83%BD%E8%A9%95%E4%BC%B0.pdf. S. M. Mccurry et al., “The Cognitive Abilities Screening Instrument (CASI): Data from a cohort of 2524 cognitively intact elderly,” International Journal of Geriatric Psychiatry, vol. 14, no. 10, pp. 882-888, 1999. 周燕, 郭起浩, and 洪震, “中文修訂版智能篩查檢測在阿爾茨海默病和輕度認知損害評估中的作用,” 中國臨床神經科學, vol. 1, 2009. "百度文庫：老年人認知功能篩查量表（CASI）," https://wenku.baidu.com/view/7f74b91959eef8c75fbfb318?_wkts_=1676213160347. Nasreddine, Montreal Cognitive Assessment (MoCA): A Compendium of Tests, Scales and Questionnaires, 2020. E. Eyigoz et al., “Linguistic markers predict onset of Alzheimer's disease,” EClinicalMedicine, vol. 28, pp. 100583, Nov, 2020. D. Helmut, V. H. Roeland, and T. Jeanine, “Lexical Richness in the Spontaneous Speech of Bilinguals,” Applied Linguistics, no. 2, pp. 197-222, 2003. R. S. Bucks et al., “Analysis of spontaneous, conversational speech in dementia of Alzheimer type: Evaluation of an objective technique for analysing lexical performance,” Aphasiology, vol. 14, no. 1, pp. 71-91, 2000. O. Levy, and Y. Goldberg, "Neural Word Embedding as Implicit Matrix Factorization." pp. 2177-2185. W.-Y. Ma, and K.-J. Chen, "Introduction to CKIP Chinese word segmentation system for the first international Chinese word segmentation bakeoff." pp. 168-171. M.-Y. Day, and C.-C. Lee, "Deep learning for financial sentiment analysis on finance news providers." pp. 1127-1134. Y. Goldberg, and O. Levy, “word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method,” arXiv preprint arXiv:1402.3722, 2014. V. Sze et al., “Efficient processing of deep neural networks: A tutorial and survey,” Proceedings of the IEEE, vol. 105, no. 12, pp. 2295-2329, 2017. D. Beltrami et al., “Speech analysis by natural language processing techniques: a possible tool for very early detection of cognitive decline?,” Frontiers in aging neuroscience, vol. 10, pp. 369, 2018. T. Mikolov et al., “Efficient Estimation of Word Representations in Vector Space,” Computer Science, 2013. J. Fritsch, S. Wankerl, and E. Nöth, "Automatic diagnosis of Alzheimer’s disease using neural network language models." pp. 5841-5845. D. H. Hubel, and T. N. Wiesel, “Receptive fields, binocular interaction and functional architecture in the cat's visual cortex,” The Journal of physiology, vol. 160, no. 1, pp. 106, 1962. Kunihiko, and Fukushima, “Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position,” Biological Cybernetics, 1980. W. Liu et al., “Large-margin softmax loss for convolutional neural networks,” arXiv preprint arXiv:1612.02295, 2016. Y. Tang, “Deep Learning using Linear Support Vector Machines,” Computer ence, 2013. M. J. Wagner, “Transcribing, searching and data sharing: The CLAN software and the TalkBank data repository,” Gesprachsforschung : Online-Zeitschrift zur verbalen Interaktion, vol. 11, pp. 154-173, 2010. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/87100 | - |
| dc.description.abstract | 我的研究主要關於阿茲海默症（Alzheimer）的中英文資料偵測對比。國際上在中英文互相學習和研究Alzheimer有很多的困難，其中最大的是語言的溝通障礙，而研究中文資料的外國人非常少，所以我受到啟發希望通過兩種語言應用在自然語言處理領域對Alzheimer判斷進行對比，從而試圖發現其中的一些關聯，和可遷移性。
中間過程變量分析，透過改變不同的變量，可以得到不同的結果，我的研究一共有三個貢獻如下，首先是驗證在基於中文資料集進行的系統，直接使用英文資料進行遷移學習獲得結果，幫助英文使用者更快的研究使用中文的阿茲海默症潛在患者。第二是不同的翻譯軟體在本次實驗中所產生的差異，我使用谷歌翻譯、搜狗翻譯、有道翻譯三種翻譯軟體對英文文本資料翻譯，總結出最佳的一種，給涉及研究跨國語言的研究提供新的參考。第三是根據文本資料建立新的stopwords幫助切詞，對比實驗結果對於準確度的提高。比較結果之餘也與之前實驗模型的結果進行對比，確實有比較好的表現。 | zh_TW |
| dc.description.abstract | My research is mainly about comparing Chinese and English data on Alzheimer's disease detection. Since there are many difficulties in learning and studying Alzheimer's disease in English and Chinese internationally, the biggest one is the language barrier, and there are very few foreigners studying Chinese data, I was inspired to compare Alzheimer's disease judgments through two language applications in natural language processing to try to discover some correlations and transferability. The first is to validate the system based on Chinese data and directly use English data for transfer learning to obtain results that help English speakers screen potential AD patients faster in Chinese. The second point is the differences produced by different translation programs in this experiment. I used three translation programs, Google Translate, Sogou Translate, and Youdao Translate, to translate the English text material and summarize the best one to get a new reference for transnational language study. Third, new stop words are created to shorten the words according to the text information, and the experimental results are compared to improve the accuracy. The comparative results are also compared with the results of the previous experimental models, which indeed show better performance. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-05-05T17:30:41Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2023-05-05T17:30:41Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 目錄
誌謝 I 摘要 II ABSTRACT III 目錄 IV 圖目錄 VI 表目錄 VI 一、 緒論 1 1-1、 動機與目的 1 1-2、 論文架構 3 1-3、 文獻回顧 4 1-4、 貢獻 5 二、 技術背景 6 2-1、傳統偵測手段 6 2-1-1、Mini Cog 6 2-1-2、Mini-mental state examination 6 2-1-3、The Cognitive Abilities Screening Instrument 8 2-1-4、Montreal Cognitive Assessment 9 2-1-5、Boston Diagnostic Aphasia Examination 11 2-2、NLP方面的偵測應用 12 2-2-1、文本結構分析 13 2-2-2、文本的數位化-文本的分散式表示 13 2-2-3、機器學習與自然語言處理結合的應用 14 三、 研究方法 16 3-1、文本預處理 16 3-2、特徵提取 18 3-3、分類原理 18 四、 實驗與討論 19 4-1、資料集獲取 19 4-2、CNN-LSTM 20 4-3、引入Softmax細化分類[24] 24 4-4、不同翻譯軟體對比 25 4-5、遷移學習 30 4-5-1、實驗一 30 4-5-2、實驗二 33 4-5-3、實驗三 36 五、 結論 39 5-1、資料集的限制 40 5-2、設備的限制 40 5-3、實驗方法的限制 40 5-4、新增翻譯軟體 41 REFERENCE 42 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 自然語言處理 | zh_TW |
| dc.subject | 阿茲海默症 | zh_TW |
| dc.subject | 變量分析 | zh_TW |
| dc.subject | 切詞 | zh_TW |
| dc.subject | variable analysis | en |
| dc.subject | natural language processing | en |
| dc.subject | Alzheimer's disease | en |
| dc.subject | Word Segmentation | en |
| dc.title | 中英文遷移學習之阿茲海默症預測對比 | zh_TW |
| dc.title | Comparison of English and Chinese Transfer Learning for prediction of Alzheimer's disease | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 111-1 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 林書宇;黃乾綱 | zh_TW |
| dc.contributor.oralexamcommittee | Shu-yu Lin;Chien-Kang Huang | en |
| dc.subject.keyword | 阿茲海默症,變量分析,自然語言處理,切詞, | zh_TW |
| dc.subject.keyword | Alzheimer's disease,variable analysis,natural language processing,Word Segmentation, | en |
| dc.relation.page | 43 | - |
| dc.identifier.doi | 10.6342/NTU202300436 | - |
| dc.rights.note | 未授權 | - |
| dc.date.accepted | 2023-02-15 | - |
| dc.contributor.author-college | 工學院 | - |
| dc.contributor.author-dept | 工程科學及海洋工程學系 | - |
| 顯示於系所單位： | 工程科學及海洋工程學系 | |
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