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題名：數位資源之脈絡資訊探索及選用決策模式
作者：郭俊良
作者(外文)：Jiunn-LiangGuo
校院名稱：國立成功大學
系所名稱：資訊管理研究所
指導教授：王惠嘉
學位類別：博士
出版日期：2013
主題關鍵詞：脈絡資訊；語意分析；語段分析法；權重式網頁存取評量法；Contextual information；semantics；discourse analysis；weighted pagerank
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當人類進入資訊時代，文件資料的數位化逐漸改變資訊擷取的方式，同時使得知識的獲得更為便利。然而隨著網路資源的不斷累積，漸漸形成海量等級的數位資料，間接產生許多資源管理上的問題，例如：關鍵資訊的搜尋不易、文件自動化處理的難度增加以及資源管理效能降低的議題等。因此，近年來許多的研究人員投入相關的研究領域，希望運用自然語言處理（Natural Language Processing）、文件採礦（Text Mining）及資訊擷取（Information Retrieval）等技術從不同的層面針對數位文件資源進行分析，期能提出更有效率的方法來改善文件資源的運用及管理作為。
有關數位文件資源的研究範圍相當廣泛，其中文件內容分析及文件資源重要性評量等領域是近年來的重要研究議題。有關文件內容分析的研究方法，多數學者主要是針對文件中出現的字詞頻率與特性進行統計分析。然而，不論何種文件資源，文本內容及資源的選用行為具有許多面向，若單從文件的用字遣詞來探討其中的重要性，不但無法深入探究文件的意義，亦將忽略文章結構中的前後文意連貫性或前後文意的脈絡關聯所隱含的重要特性，進而將使得分析的結果在未來的應用上受到限制。另一方面，文件資源重要性評量的相關議題亦受到廣泛的重視。其中在區域學術資源(如電子期刋)選用的決策評量方面，網頁式的線上系統中所具備的超連結功能，間接提供研究人員在參考相關延伸資料時重要訊息的引導。該特性與研究論文中所引用參考資料的決策過程隱含了許多重要的前後脈絡關聯值得加以重視。
綜觀在資訊擷取領域中有關脈絡資訊（contextual information）的研究議題大致可分為兩個方向進行探討。第一個方向著重在文件內文的脈絡特徵，而另一個方向則是從數位資源運用行為的層面來進行探討。因此，本論文即針對這兩方向的議題提出探索式研究設計。首先，在第一部份的研究中所提出的方法主要是利用具語意考量的語段分析（discourse analysis）技術來檢視文本內容脈絡的連貫性及語意轉折，藉此決定全文的語意段落（discourse segment）。隨後則透過改良的特徵擷取法（feature selection），自語段中選取隱含的重要特徵 - 語段次主題 (discourse subtopics) 以形成特徵集，最後藉由自動化文件分類的實驗結果驗證該方法的成效。第二部份將檢視學術文件資源的運用模式，並建構核心期刋的評估決策模式，期能透過提出的權重式網頁存取評量法（weighted PageRank）檢視數位文件資源（如電子期刋）存取行為中的脈絡關聯性，同時結合研究者文獻引用的資訊，建構區域電子期刋評估指標 (Local Impact Factor, LIF)，以協助資源使用者在引用相關學術資料及圖書資訊管理人員未來在進行電子期刋資料庫採購工作時的決策參考。
經由本研究相關實驗結果得知，文件內容以及文件資源的選用行為的確隱含重要的脈絡資訊。透過本研究所提出的方法證明，脈絡資訊可透過設計的方法萃取並進而應用於改善自動化文件分類工作及評估重要數位資源時的決策參考。
Digital document resources possess implicit contextual information, which raises many research challenges in the information retrieval discipline. Such information remained either in the discourse context of document or in the access of web-based resources has led to the need of deep investigation on the value of contextual attributes to the widespread application of information processing. For the content of document, the contextual information is believed to be existed in the discourse segments of text, which has long been treated as difficult issue because of the diversified document structure. On the other hand, the contextual information occurred in the access of web resources is even more difficult to be explored because it involves the unpredictable human behavior and the varied background knowledge. In addition, such a circumstance makes monitoring the user decision-making process even more complicated because the usage of resource is untraceable. However, contextual information has long been treated as important pattern which is believed to be a critical factor to improving the performance of information processing.
Regarding the analysis of contextual information, this work aims to propose two novel approaches on the exploration of contextual information existed in both textual level and web access aspects by means of adopting discourse structure analysis and designing a core decision model, respectively. For textual resources, this study designs a framework to detect the context by analyzing the discourse structure not only addressing the shifts and continuity of coherent subtopics but also exploiting the syntactic attributes, which are capable of enhancing the performance of text classification. To inspect the validation, the first model will implement to e-book classification task to testify the contribution of the explored contextual information. For the web access aspect, the second study focuses on the local access of digital library and proposes a novel system - the Local Impact Factor (LIF) to evaluate and rank the importance of digital resources. The system investigates the requirements of local user community as incorporating both the access rate of adopted journals and the weighted impact factor technique to capture the contextual information existed between the usages of resources and citation of thesis. And, by measuring the citation information from the local users’ articles, it helps reveal the relationship between the download of resources and the real application of the citation decision.
Both studies are fully implemented and tested on two real-world datasets together with a series of integrated experiments. As the result, the evaluations have demonstrated the vital role of contextual information existed in both textual and web resources and the significant improvement in performance is also revealed. Also, our proposed methods are proven to be feasible and beneficial for future information processing applications.
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