Electronic Theses and Dissertations (ETDs) contain valuable scholarly information that can be of immense value to the scholarly community. Millions of ETDs are now publicly available online, often through one of many digital libraries. However, since a majority of these digital libraries are institutional repositories with the objective being content archiving, they often lack end-user services needed to make this valuable data useful for the scholarly community. To effectively utilize such data to address the information needs of users, digital libraries should support various end-user services such as document search and browsing, document recommendation, as well as services to make navigation of long PDF documents easier. In recent years, with advances in the field of machine learning for text data, several techniques have been proposed to support such end-user services. However, limited research has been conducted towards integrating such techniques with digital libraries. This research is aimed at building tools and techniques for discovering and accessing the knowledge buried in ETDs, as well as to support end-user services for digital libraries, such as document browsing and long document navigation. First, we review several machine learning models that can be used to support such services. Next, to support a comprehensive evaluation of different models, as well as to train models that are tailored to the ETD data, we introduce several new datasets from the ETD domain. To minimize the resources required to develop high quality training datasets required for supervised training, a novel AI-aided annotation method is also discussed. Finally, we propose techniques and frameworks to support the various digital library services such as search, browsing, and recommendation. The key contributions of this research are as follows: - A system to help with parsing long scholarly documents such as ETDs by means of object-detection methods trained to extract digital objects from long documents. The parsed documents can be used for further downstream tasks such as long document navigation, figure and/or table search, etc. - Datasets to support supervised training of object detection models on scholarly documents of multiple types, such as born-digital and scanned. In addition to manually annotated datasets, a framework (along with the resulting dataset) for AI-aided annotation also is proposed. - A web-based system for information extraction from long PDF theses and dissertations, into a structured format such as XML, aimed at making scholarly literature more accessible to users with disabilities. - A topic-modeling based framework to support exploration tasks such as searching and/or browsing documents (and document portions, e.g., chapters) by topic, document recommendation, topic recommendation, and describing temporal topic trends. / Doctor of Philosophy / Electronic Theses and Dissertations (ETDs) contain valuable scholarly information that can be of immense value to the research community. Millions of ETDs are now publicly available online, often through one of many online digital libraries. However, since a majority of these digital libraries are institutional repositories with the objective being content archiving, they often lack end-user services needed to make this valuable data useful for the scholarly community. To effectively utilize such data to address the information needs of users, digital libraries should support various end-user services such as document search and browsing, document recommendation, as well as services to make navigation of long PDF documents easier and accessible. Several advances in the field of machine learning for text data in recent years have led to the development of techniques that can serve as the backbone of such end-user services. However, limited research has been conducted towards integrating such techniques with digital libraries. This research is aimed at building tools and techniques for discovering and accessing the knowledge buried in ETDs, by parsing the information contained in the long PDF documents that make up ETDs, into a more compute-friendly format. This would enable researchers and developers to build end-user services for digital libraries. We also propose a framework to support document browsing and long document navigation, which are some of the important end-user services required in digital libraries.

The "digital library" (DL) is a communication technology that has the potential to improve communication by removing temporal and geographic barriers and by introducing interactivity. This research focused on the adoption of digital libraries for electronic theses and dissertations (ETD-DL) at universities worldwide. ETD-DLs provide a means for universities to learn about implementing digital libraries in a networked environment.This research used diffusion of innovation theory to explore what has influenced ETD-DL adoption among Networked Digital Library of Theses and Dissertations (NDLTD) member universities. Communication channels were categorized as being either interpersonal or mediated. The perceived importance of these channels was assessed both within and between organizations. Although ETD-DL adoption is an organizational level decision it has implications for both the university and members of the university community. In some cases, these outcomes may be in conflict; for example, what is best for the organization may result in complications for an individual. Therefore the perceived importance of two innovation attributes, relative advantage and compatibility, were examined at both the collective and individual level.This study employed a web-based survey to collect data from the 133 universities in 26 countries that were NDLTD members in December 2002. Respondents were members of the university's "ETD Committee" and represented academic administrators, faculty, librarians, and computer systemsspecialists. Surveys were received from 95 respondents representing 65 universities in 14 countries. Twenty-one of these universities were outside the United States. Universities were from countries with a wide range of economic development.Results provide insights into university attitudes towards a technological innovation for knowledge dissemination. For example, results suggest that interpersonal channels of communication are more important than mediated channels within the organization. Additionally, interpersonal channels are more important for communication within the organization than between organizations. However, mediated channels of communication are more important for those universities that have decided to adopt the ETD-DL but have not yet implemented the union catalog or self-archiving options. There were also significant differences in the importance attributed to these channels by individuals in different jobs. The results also suggest strategies that could encourage development of digital libraries within a social system.

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. 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.

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.

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.