因應近年來數位典藏的趨勢日漸發展，越來越多珍貴中文歷史文本 選擇進行數保存，而保存的同時會面對文本的作者遺失或從缺，進而 影響文本的完整性，而本論文提出了一個適用於中文史料文本作者分 析的方法，主要是透過語言模型的建構，為每一位潛在的作者訓練出 一個專屬的語言模型，而搭配不同的平滑方法能避免掉某一受測文本 單詞出現的機率為零的機率進而造成計算上的錯誤，而本論文主要採 用改良式 Kneser–Ney 平滑方法，該平滑方法因其會同時考慮到 N 詞彙 語言模型的高低頻詞的影響，而使其成為建構語言模型普遍選擇的平 滑方式。 若僅將每一位潛在作者的所有文章進行合併訓練成單一的語言模型 會忽略掉許多特性，所以本篇論文在取得附有價值的歷史文本之外， 又加入後設資料 (Metadata) 進行綜合分析，包括人工標記的主題分類 的統計資訊，使建構出來的語言模型更適配受測文本，增加預測結果 的準確性。和加入額外的自定義的字詞以符合文本專有名詞的用詞習 慣，還會在一般建構語言模型的基礎上，加入長字詞的權重，以確定 字詞長度對預測準確度的關係。最後還會採用遞歸神經網路 (Recursive neural networks) 結合語言模型進行作者預測，與傳統的語言模型分析 作進一步的比較。 / In recent years, the trend of digital collections has been developing day by day, and more and more precious Chinese historical corpora have been selected for preservation. The preservation of the corpora at the same time will face the loss or lack of the authors, thus affecting the integrity of the corpora. A method for analyzing the author of the Chinese historical text is mainly through the construction of the language model, for each potential author to train a specific language model, and with a different smoothing method can be avoided zero probability of words and the error is caused by the calculation. This paper mainly adopts the Interpolated Modified Kneser-Ney smoothing method, which will take into account the influence of higher order and lower order n-grams string frequency. So, Interpolated Modified Kneser-Ney smoothing is become a very popular way to construct a general choice of language models. The combination of all the articles of each potential author into a single language model will ignore many of the features, so this paper in addition to the value of the historical corpora, but also to add the metadata to integrate analysis, including the statistical information of the subject matter classification of the artificial mark, so that the constructed language model is more suitable for the measured text, increase the accuracy of the forecast results, add additional custom words to match the language of the proper nouns, in addition. But also on the basis of the general construction language model, the weight of the long word to join, to determine the length of the word on the relationship between the accuracy of prediction. Finally, recursive neural networks language models are also used to predict the authors and to make further comparisons with the traditional language model analysis.

近年智慧型手機與網路的普及，使得社群網站與線上串流音樂蓬勃發展。臉書（Facebook）用戶截至去年止每月總體平均用戶高達18.6億人 ，粉絲專頁成為公司企業特別關注的行銷手段。粉絲專頁上的貼文能夠在短時間內經過點閱、分享傳播至用戶的頁面，達到比起電視廣告更佳的效果，也節省了許多的成本。本研究提供了一套針對臉書粉絲專頁貼文的分群流程，考量到貼文字詞的複雜性，除了抓取了臉書粉絲專頁的貼文外，也抓取了與其相關的KKBOX網頁資訊，整合KKBOX網頁中的資料，對中文斷詞系統（Jieba）的語料庫進行擴充，以提高斷詞的正確性，接著透過雙分群演算法（Minimum Squared Residue Co-Clustering Algorithm）對貼文進行分群，並利用鑑別率（Discrimination Rate）與凝聚率（Agglomerate Rate）配合主成份分析（Principal Component Analysis）所產生的分佈圖來對分群結果進行評估，選出較佳的分群結果進一步去分析，進而找出分類的根據。在結果中，發現本研究的方法能夠有效的區分出不同類型的貼文，甚至能夠依據使用字詞、語法或編排格式的不同來進行分群。 / In recent years, because both smartphones and the Internet have become more popular, social network sites and music streaming services have grown vigorously. The monthly average of Facebook users hit 1.86 billion last years and Facebook Fan Page has become a popular marketing tool. Posts on Facebook can be broadcasted to millions of people in a short period of time by LIKEing and SHAREing pages. Using Facebook Fan Page as a marketing tool is more effective than advertising on television and can definitely reduce the costs. This study presents a process to cluster posts on Facebook Fan Page. Considering the complicated word usage, we grasped information on Facebook Fan Page and related information on the KKBOX website. First, we integrated the information on the website of KKBOX and expanded the text corpus of Jibea to enhance the accuracy of word segmentation. Then, we clustered the posts into several groups through Minimum Squared Residue Co-Clustering Algorithm and used discrimination Rate and Agglomerate Rate to analyze the distribution chart of Principal Component Analysis. After that, we found the suitable classification and could further analyze it. How posts are classified can then be found. As a result, we found that the method of this study can effectively cluster different kinds of posts and even cluster these posts according to its words, syntax and arrangement.

本研究旨在探討台灣英文教科書與學生英文作文佳作中，敘述文與說明文中的因果關係使用。因為因果關係在閱讀與寫作中很常見，所以對學生來說，學會如何正確使用因果關係是件極為重要的事。根據李(2003)的研究，她發現因果關係對學生來說是最難學好的因果關係。因此，本研究希望能了解教科書中的敘述文與說明文是如何介紹因果關係給學生。同時，本研究亦希望能得知學生如何在不同文體的英文作文中，使用因果關係。 本研究選用三個版本的高中英文教科書來進行研究，之所以選擇此三版本教科書的原因在於，此三個版本在台灣高中英文課本的市占率最高。而又因為敘述文和說明文在學測和指考的英文作文考試中是最常要測驗學生的文體，因此本研究進一步挑選課本中的敘述文和說明文來進行分析與探討。此外，本研究亦研究民國100年到105年的學測、指考英文作文佳作，舉凡是敘述文和說明文中所使用的因果關係都進行分析與探討。 研究結果顯示在三個版本中，敘述文和說明文的分布無明顯差異。此外，本文發現年級越高的高中生會接觸到愈多的敘述文，但愈少的說明文。研究結果亦顯示文體會影響因果關係的使用情形。相較於敘述文中的因果關係使用，我們發現教科書作者在說明文中使用較多因果關係。 而我們也發現在不同文體中的不同文章結構，因果關係使用的情形不同。在敘述文中，因果關係主要被使用於Complications和Resolutions中。在說明文中，因果關係則主要被使用於Arguments supported by evidence 和Summing up the position中。除此之外，本文亦發現在四類因果連接詞中，Reason和Result這兩類的因果連接詞最常被使用於敘述文和說明文中。 為了清楚看出因果關係，本文針對包含因果關係的句子皆進行了句法、時間及邏輯順序的分析。結果發現幾乎所有因果關係中的時間順序皆與邏輯順序一致。同時，本文亦歸納出教科書中的因果關係的七種使用形式，並提供教科書中的例句來進行討論。 最後，透過學生英文作文佳作的分析，我們發現在因果關係的使用量方面，這群學生與教科書作者相似。而本研究亦歸納出學生在佳作中的四個因果關係誤用的問題。 希望本研究的發現及所提供的建議可供台灣高中英文科教師、教科書作者以及高中生參考。 / The purpose of the study is to investigate the variations of causal relations in narrations and expositions in Taiwan senior high school's English textbooks, and high performance students’compositions. Since causal relations are common in reading and writing, it is important for students to learn to use it. According to Lee (2003), causal relations are the most difficult logical relations for students to master. The present study wants to know how causal relations are introduced to learners in the narrations and expositions from textbooks. Besides, the present study also wants to know how students use causal relations in different genres of compositions. In the study, three series of textbooks are selected for the examination. These three textbooks were chosen as they are commonly used textbooks in Taiwan senior high schools. The selected texts for examination are the texts of two genres: narration and exposition, for these two genres are frequently tested in College Entrance Exams. Moreover, the causal relations in narrations and expositions among the high-scoring essays in years of 2011 to 2016College Entrance Exams were examined as well. The results showed that three series of textbooks in have no significant differences in the distributions of narrations and expositions. Besides, senior high school students expose more narrations but fewer expositions in the higher grades. Furthermore, the result revealed that genres do influence the uses of causal relations, for expositions in the textbooks applied more causal relations than the narrations in the textbooks. Moreover, different usages among different text organizations in both narrations and expositions were found. In the text organizations of narrations, causal relations were mainly used in complications and resolutions.In the text organizations of expositions, causal relations were mainly used in Arguments supported by evidence and Summing up the position. The types of causal relations which are frequently used in textbooks were also found as well. The results showed that Reason and Result type of causal logical connectors were mainly used in both narrations and expositions. Analyses of casual relations’sequences were conducted to clearly see the syntactic sequences, temporal sequences, and logical sequences of the sentences with causal relations. It was found that almost all the causal relations in the textbooks had the temporal sequences equaled to the logical sequences. Moreover, six usages of causal logical connectors were categorized and presented with example sentences. Last but not least, through the analyses of causal relations in students’high-scoring essays, the result showed that high performance students used the similar amounts of causal relations in their compositions with the textbooks’ writers. The present study also categorizes the errors of misusing the causal relations into four problems. It is hoped that this study can provide insights for the senior high school English teachers, textbook publishers, and students to follow.

京都大学 / 新制・課程博士 / 博士(人間・環境学) / 甲第23274号 / 人博第989号 / 新制||人||234(附属図書館) / 2020||人博||989(吉田南総合図書館) / 京都大学大学院人間・環境学研究科共生人間学専攻 / (主査)教授 谷口 一美, 准教授 守田 貴弘, 教授 定延 利之 / 学位規則第4条第1項該当 / Doctor of Human and Environmental Studies / Kyoto University / DFAM

京都大学 / 新制・課程博士 / 博士(人間・環境学) / 甲第23581号 / 人博第1013号 / 新制||人||239(附属図書館) / 2021||人博||1013(吉田南総合図書館) / 京都大学大学院人間・環境学研究科共生人間学専攻 / (主査)教授 谷口 一美, 准教授 守田 貴弘, 准教授 金丸 敏幸, 教授 松本 曜 / 学位規則第4条第1項該当 / Doctor of Human and Environmental Studies / Kyoto University / DFAM

京都大学 / 0048 / 新制・課程博士 / 博士(人間・環境学) / 甲第18367号 / 人博第680号 / 新制||人||163(附属図書館) / 25||人博||680(吉田南総合図書館) / 31225 / 京都大学大学院人間・環境学研究科共生人間学専攻 / (主査)教授 東郷 雄二, 教授 服部 文昭, 准教授 谷口 一美 / 学位規則第4条第1項該当 / Doctor of Human and Environmental Studies / Kyoto University / DGAM

京都大学 / 0048 / 新制・課程博士 / 博士(文学) / 甲第19431号 / 文博第709号 / 新制||文||630(附属図書館) / 32467 / 京都大学大学院文学研究科行動文化学専攻 / (主査)教授 吉田 豊, 教授 田窪 行則, 准教授 千田 俊太郎 / 学位規則第4条第1項該当 / Doctor of Letters / Kyoto University / DFAM

京都大学 / 新制・課程博士 / 博士(人間・環境学) / 甲第23967号 / 人博第1019号 / 新制||人||240(附属図書館) / 2022||人博||1019(吉田南総合図書館) / 京都大学大学院人間・環境学研究科共生人間学専攻 / (主査)准教授 守田 貴弘, 教授 谷口 一美, 准教授 堀口 大樹 / 学位規則第4条第1項該当 / Doctor of Human and Environmental Studies / Kyoto University / DFAM

京都大学 / 新制・課程博士 / 博士(文学) / 甲第24957号 / 文博第927号 / 新制||文||734(附属図書館) / 京都大学大学院文学研究科文献文化学専攻 / (主査)教授 中村 唯史, 教授 家入 葉子, 准教授 堀口 大樹 / 学位規則第4条第1項該当 / Doctor of Letters / Kyoto University / DFAM

京都大学 / 新制・課程博士 / 博士(人間・環境学) / 甲第25376号 / 人博第1118号 / 京都大学大学院人間・環境学研究科共生人間学専攻 / (主査)教授 河﨑 靖, 准教授 西脇 麻衣子, 准教授 堀口 大樹, 教授 CATT Adam Alvah / 学位規則第4条第1項該当 / Doctor of Human and Environmental Studies / Kyoto University / DGAM