English  |  正體中文  |  简体中文  |  Post-Print筆數 : 11 |  Items with full text/Total items : 88613/118155 (75%)
Visitors : 23471801      Online Users : 235
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    政大機構典藏 > 商學院 > 統計學系 > 學位論文 >  Item 140.119/118935
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/118935


    Title: 關鍵詞與階層式詞彙文本分群之應用
    The Application of Key Words and Hierarchical Vocabulary Text Grouping
    Authors: 黃培軒
    Huang, Pei-Hsuan
    Contributors: 余清祥
    宋皇志

    Yue, Ching-Syang
    Sung, Huang-Chin

    黃培軒
    Huang, Pei-Hsuan
    Keywords: 階層式詞彙文本分群
    關鍵詞
    數位人文
    語意分析
    資料導向
    Hierarchical vocabulary text grouping
    Keywords
    Digital humanities
    Semantic analysis
    Data driven
    Date: 2018
    Issue Date: 2018-07-27 11:33:35 (UTC+8)
    Abstract: 文本為人類歷史足跡的載體,從朝代歷史至個人日記,記錄著當代人類的文化思想、風俗民情與科技發展,隨著時代推演這些紀錄不再侷限於牛皮紙張或土瓦竹簡等實體載具,以更多元的數位型式記載在網路虛擬世界。而文本往往必須委由專家才能解讀出其中心思想,隨著文字分析技術的興起,愈來愈多學者研發藉由量化技術找出文字蘊含的意義,以因應資訊氾濫時代中快速篩選資訊,提供專家以外另一種角度的解讀。
    主題式分析是文字分析的重要研究議題,透過界定關鍵詞與區隔文本屬性使得文本解析更為精確及有效率,本文以常用的TF-IDF (term frequency inverse document frequency)與處理語意的常見工具詞網(WordNet)為基礎,提出核心詞彙與篩選標籤特徵應用,探討因文章長短所造成的不穩定性與特殊領域詞彙關係問題(Magnini and Cavaglia, 2000)。本文利用《臺灣社會科學引文索引》(TSSCI)、美國專利、《人民日報》等三個文本作為分析對象,建構該文本的語意關係與相關之應用。分析發現TSSCI與美國專利的文本的分類準確率近八成,但若文本篇數過少時會因為雜訊太強無法呈現語意關係;而文本標籤(Label)間若是風格寫作上的差異,本文提出的主題分類無法歸類出較準確的分類結果,這可能也是《人民日報》文本分類準確率不佳的原因,但仍能透過該標籤的特徵(Feature)了解該時期的特殊主題。
    Text is the carrier of the human history. From the official history to the personal diary, it records the culture, thoughts, customs, and technological developments of human beings. With the progress of computer technology, text recordings are no longer restricted to physical vehicles, such as kraft paper or earthen bamboo slips, and they can be recorded in various digital forms. With the rise of interest in quantifying text analysis, more and more scholars are dedicated in the technologic development of text analysis and apply them to explore the text meaning. Many people think that computer technology, such as machine learning and artificial intelligence, can help us relax the burden of human experts in seeking the meaning under the text.
    Topic analysis is an important research topic in text analysis. It makes text parsing faster by defining keywords and separating text attributes. This paper proposes the application of core vocabulary and screening tag features based on the commonly used TF-IDF (term frequency inverse document frequency) and the common tool word network (WordNet). We will apply them in exploring the relationship between instability caused by the length of the article and vocabulary (Magnini and Cavaglia, 2000). We use the Taiwan Social Science Citation Index (TSSCI), the U.S. patent, and the People's Daily as the study materials. The results of text analysis show that the classification accuracies of TSSCI and U.S. patent texts are nearly 80%. However, if the number of article is too small, then the noise will distort the analysis and semantic relations. Also, we found the style writing would influence the accuracy of topic classification, which may be the reason why the People’s Daily text classification accuracy is not good.
    Reference: 何立行、余清祥、鄭文惠(2014),從文言到白話:《新青年》雜誌語言
    變化統計研究,東亞觀念史集刊,第七期,頁427-454。
    余清祥(1998),統計在紅樓夢的應用,政大學報,第七十六期,頁303-327。
    吳旻璁(2013),結合主題資訊萃取關鍵詞和建構概念圖,碩士論文,國立雲林科技大學,資訊管理研究所。
    吳怡瑾、方友杉、喻欣凱(2009),運用文件分群與概念關聯分析技術協助網誌瀏覽:任務導向評估方法,圖書資訊學研究,第四期第一卷,頁133-164。
    梁家安(2016),從國共內戰到改革開放:人民日報風格變遷之量化研究,碩士論文,國立政治大學,統計研究所。
    謝博行(2013),局部最長連續共同子序列與新詞組收集,碩士論文,國立清華大學,統計學研究所
    Beliga, S., Meštrović, A., Martinčić-Ipšić, S.(2015). An overview of graph-based keyword extraction methods and approaches. Journal of information and organizational sciences, 39(1), 1-20.
    Benezeth, Y., Bertaux, A. Manceau, A.(2015). Bag-of-word based brand recognition using Markov clustering algorithm for codebook generation. 2015 IEEE International Conference on Image Processing (ICIP), Quebec City, QC, 3315-3318.
    Chen, C.H.(2017). Improved TF.IDF in Big News Retrieval: An Empirical Study. Pattern Recognition Letters, 93, 113 - 122.
    Condon, A., Karp, R. M.(2001). Algorithms for graph partitioning on the planted partition model. Random Structures and Algorithms, 18(2):116–140.
    Donetti, L., Munoz,M. A.(2004). Detecting network communities: a new systematic and efficient algorithm. Journal of Statistical Mechanics, 2004(10):10012.
    Girvan, M., Newman, M. E. J.(2002), Community structure in social and biological networks. Proc. Natl Acad. Sci. USA 99, 7821-7826
    Hotho, A., Staab, S., Stumme, G.(2003). Wordnet improves text document clustering. In Proc. of the SIGIR 2003 Semantic Web Workshop, pages 541–544.
    Huang, A.(2008). Similarity Measures for Text Document Clustering, NZCSRSC 2008, Christchurch, New Zealand.
    Inmon, W. H., Nesavich, A.(2008). Tapping Into Unstructured Data-Integrating Unstructured Data and Textual Analytics into Business Intelligence, Prentice Hall.
    Lan, M., Tan, C.L., Low, H.B., Sung S.Y.(2005). A comprehensive
    comparative study on term weighting schemes for text categorization
    with support vector machines. In Proc. 14th WWW, 1032–1033.
    Magnini, B. and Cavaglia, G.(2000). Integrating subject field codes into wordnet. In Proceedings of LREC-2000, the Second International Conference on Language Resources and Evaluation. Athens, Greece.
    Michael W., Berry, (2004). Survey of Text Mining – Clustering, Classification, and Retrieval. Springer Press
    Newman, M. E. J.(2004), Fast algorithm for detecting community structure in networks. Physical Review E, 69(6):066133.
    Passos A. and Wainer J.(2009) Wordnet-based metrics do not seem to help document clustering.
    Pons, P., Latapy, M(2006)., Computing communities in large networks using random walks. Journal of Graph Algorithms Applications, 10(2).
    Recupero, D. R.(2007). A new unsupervised method for document clustering by using WordNet lexical and conceptual relations. Information Retrieval, 10(6), 563– 579.
    Salton, G., Yu, C. T.(1975). On the construction of effective vocabularies for information retrieval[J]. ACM Sigplan Notices, 9(3), 48-60.
    Shraddha K. P., Pramod B. D.(2017). Vishakha A. M., Hierarchical document clustering based on cosine similarity measure.
    Description: 碩士
    國立政治大學
    統計學系
    105354027
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0105354027
    Data Type: thesis
    DOI: 10.6814/THE.NCCU.STAT.011.2018.B03
    Appears in Collections:[統計學系] 學位論文

    Files in This Item:

    File SizeFormat
    402701.pdf1883KbAdobe PDF0View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告
    1.本網站之數位內容為國立政治大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,惟仍請適度,合理使用本網站之內容,以尊重著作權人之權益。商業上之利用,則請先取得著作權人之授權。
    2.本網站之製作,已盡力防止侵害著作權人之權益,如仍發現本網站之數位內容有侵害著作權人權益情事者,請權利人通知本網站維護人員(nccur@nccu.edu.tw),維護人員將立即採取移除該數位著作等補救措施。
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback