Great progress has been made to leverage the improvements made in natural language processing and machine learning to better mine data from journals, conference proceedings, and other digital library documents. However, these advances do not extend well to book-length documents such as electronic theses and dissertations (ETDs). ETDs contain extensive research data; stakeholders -- including researchers, librarians, students, and educators -- can benefit from increased access to this corpus. Challenges arise while working with this corpus owing to the varied nature of disciplines covered as well as the use of domain-specific language. Prior systems are not tuned to this corpus. This research aims to increase the accessibility of ETDs by the automatic classification of chapters of an ETD using machine learning and deep learning techniques. This work utilizes an ETD-centric target classification system. It demonstrates the use of custom trained word and document embeddings to generate better vector representations of this corpus. It also describes a methodology to leverage extractive summaries of chapters of an ETD to aid in the classification process. Our findings indicate that custom embeddings and the use of summarization techniques can increase the performance of the classifiers. The chapter-level labels generated by this research help to identify the level of interdisciplinarity in the corpus. The automatic classifiers can also be further used in a search engine interface that would help users to find the most appropriate chapters. / Master of Science / Electronic Theses and Dissertations (ETDs) are submitted by students at the end of their academic study. These works contain research information pertinent to a given field. Increasing the accessibility of such documents will be beneficial to many stakeholders including students, researchers, librarians, and educators. In recent years, a great deal of research has been conducted to better extract information from textual documents with the use of machine learning and natural language processing. However, these advances have not been applied to increase the accessibility of ETDs. This research aims to perform the automatic classification of chapters extracted from ETDs. That will reduce the human effort required to label the key parts of these book-length documents. Additionally, when considered by search engines, such categorization can aid users to more easily find the chapters that are most relevant to their research.

In Deutschland stehen vor allem die Universitätsbibliotheken zu Beginn des 21. Jahrhunderts vor der großen Herausforderung, die sich rasch vollziehenden Veränderungen und die sich bietenden technischen Möglichkeiten zu erkennen und als Chance zu begreifen, sich vom Image angestaubter Archivieranstalten zu lösen und sich nach und nach zu ”universitären Informations- und Servicezentren” zu entwickeln. Auch für die Sächsische Landesbibliothek - Staats- und Universitätsbibliothek (SLUB - im Folgenden nur noch SLUB genannt) trifft diese Standortbestimmung zu. Hier machte man sich etwa ab dem Jahr 1999 verstärkt darüber Gedanken, wie man in Zukunft Hochschulschriften digital verwalten und archivieren könnte.

Supporting graduate students as authors is one of the many services we provide at the University Library, University of Saskatchewan (USask). Graduate students often submit articles to journals based on content from their electronic theses or dissertations (ETDs). Recently, we have noticed an increase in the number of such article submissions being flagged for possible rejection on “plagiarism” or “prior publication” grounds. We suspect this may be because plagiarism detection software is increasingly being integrated into publishers’ article submission systems. This software is triggered by the existence of the student’s open access (OA) ETD in our institutional repository. This happens despite OA ETD inclusion in repositories being a common practice and despite journal policies often allowing submission of articles based on ETDs. We review common practices and guidelines around publishing of ETD content, two recent cases of journals initially rejecting such submissions by graduate student authors of our institution, and our reflections on this issue and how to address it.

Much research work has been done to extract data from scientific papers, journals, and articles. However, Electronic Theses and Dissertations (ETDs) remain an unexplored genre of data in the research fields of natural language processing and machine learning. Moreover, much of the related research involved data that is in the English language. Arabic data such as news and tweets have begun to receive some attention in the past decade. However, Arabic ETDs remain an untapped source of data despite the vast number of benefits to students and future generations of scholars. Some ways of improving the browsability and accessibility of data include data annotation, indexing, parsing, translation, and classification. Classification is essential for the searchability and management of data, which can be manual or automated. The latter is beneficial when handling growing volumes of data. There are two main roadblocks to performing automatic subject classification on Arabic ETDs. The first is the unavailability of a public corpus of Arabic ETDs. The second is the Arabic language’s linguistic complexity, especially in academic documents. This research presents the Otrouha project, which aims at building a corpus of key metadata of Arabic ETDs as well as providing a methodology for their automatic subject classification. The first goal is aided by collecting data from the AskZad Digital Library. The second goal is achieved by exploring different machine learning and deep learning techniques. The experiments’ results show that deep learning using pretrained language models gave the highest classification performance, indicating that language models significantly contribute to natural language understanding. / M.S. / An Electronic Thesis or Dissertation (ETD) is an openly-accessible electronic version of a graduate student’s research thesis or dissertation. It documents their main research effort that has taken place and becomes available in the University Library instead of a paper copy. Over time, collections of ETDs have been gathered and made available online through different digital libraries. ETDs are a valuable source of information for scholars and researchers, as well as librarians. With the digitalization move in most Middle Eastern Universities, the need to make Arabic ETDs more accessible significantly increases as their numbers increase. One of the ways to improve their accessibility and searchability is through providing automatic classification instead of manual classification. This thesis project focuses on building a corpus of metadata of Arabic ETDs and building a framework for their automatic subject classification. This is expected to pave the way for more exploratory research on this valuable genre of data.

學術論文是學術研究的成果，經整理典藏並提供資源共享與利用，不僅鼓勵學術的蓬勃發展，同時也代表各學科領域的發展情況。隨著資訊科技的發展，近來國內外各學術單位對學術論文的數位典藏工作日益重視，致力於建置博碩士論文專屬資訊系統與電子論文徵集政策的推行，期盼能透過博碩士論文資訊的共建共享，長久典藏並分享論文資訊。 　　基於上述，本研究之主要目的即針對我國各學術單位在博碩士論文數位數位典藏的現況，以及資訊系統的發展模式進行瞭解，並進一步分析各單位在推動博碩士論文數位典藏工作時政策制訂的觀點，並歸納整理提出未來其他單位在發展博碩士論文數位典藏工作時的建議。 　　研究結果發現，目前國內各學術單位在博碩士論文數位典藏工作的發展還在起步階級，尚無具體的合作典藏計劃；而資訊系統的發展模式主要分為：委外資訊廠商開發、使用免費共享系統、館內自行開發系統三種，依據經費多寡而有不同的考量；各單位會因組織編制與經費成本，影響其在博碩士論文數位典藏工作參與人員、系統發展模式、博碩士論文數位化方式、電子博碩士論文呈繳方式、系統維護管理等方面的政策制訂。 　　在研究結論中歸納出八點如下：一、博碩士論文數位典藏工作參與人員包括行政單位人員及學者專家；二、資訊系統發展主要有三種模式；三、博碩士論文數位化以Acrobat軟體所製作的PDF檔格式為主；四、研究生透過資訊系統線上呈繳電子論文檔；五、博碩士論文數位典藏系統具備全文檢索功能及與書目資料庫連結查詢的機制；六、數位典藏維護工作需定期備份以降低資料毀損的風險；七、全國性的博碩士論文數位化合作典藏機制亟待建立；八、學位論文著作權及電子傳播之相關法律問題尚待釐清。最後也提出四點建議：一、建立全國性合作典藏機制，進行徵集與維護數位化論文的工作；二、依組織特色擬訂博碩士論文數位典藏政策；三、釐清博碩士論文數位典藏的合理使用範圍；四、開發博碩士論文數位典藏系統加值服務功能；以提供其他單位在政策擬訂時的參考。

In Deutschland stehen vor allem die Universitätsbibliotheken zu Beginn des 21. Jahrhunderts vor der großen Herausforderung, die sich rasch vollziehenden Veränderungen und die sich bietenden technischen Möglichkeiten zu erkennen und als Chance zu begreifen, sich vom Image angestaubter Archivieranstalten zu lösen und sich nach und nach zu ”universitären Informations- und Servicezentren” zu entwickeln. Auch für die Sächsische Landesbibliothek - Staats- und Universitätsbibliothek (SLUB - im Folgenden nur noch SLUB genannt) trifft diese Standortbestimmung zu. Hier machte man sich etwa ab dem Jahr 1999 verstärkt darüber Gedanken, wie man in Zukunft Hochschulschriften digital verwalten und archivieren könnte.

Cited-works-lists in Humanities dissertations are typically the result of five years of work. However, despite the long-standing tradition of reference mining, no research has systematically untapped the bibliographic data of existing electronic thesis collections. One of the main reasons for this is the difficulty of creating a tagged gold standard for the around 300 pages long theses. In this short paper, we propose a page-based random forest (RF) prediction approach which uses a new corpus of Literary Studies Dissertations from Germany. Moreover, we will explain the handcrafted but computationally informed feature-selection process. The evaluation demonstrates that this method achieves an F1 score of 0.88 on this new dataset. In addition, it has the advantage of being derived from an interpretable model, where feature relevance for prediction is clear, and incorporates a simplified annotation process.

Title from PDF of title page. / Document formatted into pages; contains 215 pages. / Originally submitted in HTML and can be accessed at http://www.lib.usf.edu/ETD-db/theses/available/etd-04102002-122814/unrestricted/default.htm / Thesis (Ph.D.)--University of South Florida, 2002. / Includes bibliographical references. / Text (Electronic thesis) in PDF format. / ABSTRACT: Electronic theses and dissertations (ETDs) are an evolving genre of graduate student research that is gaining widespread acceptance among universities in the international community. ETDs are also beginning to diffuse slowly among American universities; however, a number of issues continue to work against more rapid adoption among intitutions in the United States. / ABSTRACT: This dissertation examines ETDs as an evolving electronic research genre by (1) historicizing the situated development of its predecessor, the traditional print dissertation, in nineteenth century German and American Universities; (2) reporting on the current state of the Networked Digital Library of Electronic Theses and Dissertations, an initiative of Virginia Polytechnic University; (3) analyzing ETDs as a technological innovation undergoing the diffusion process according to Emmet Roger's Diffusion of Innovation Theory; and (4) presenting the results of an ETD pilot project case study carried out at the University of South Florida. / System requirements: World Wide Web browser and PDF reader. / Mode of access: World Wide Web.