Building datasets to support information extraction and structure parsing from electronic theses and dissertations

IF 1.6 Q2 INFORMATION SCIENCE & LIBRARY SCIENCE International Journal on Digital Libraries Pub Date : 2024-05-03 DOI:10.1007/s00799-024-00395-4
William A. Ingram, Jian Wu, Sampanna Yashwant Kahu, Javaid Akbar Manzoor, Bipasha Banerjee, Aman Ahuja, Muntabir Hasan Choudhury, Lamia Salsabil, Winston Shields, Edward A. Fox
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Abstract

Despite the millions of electronic theses and dissertations (ETDs) publicly available online, digital library services for ETDs have not evolved past simple search and browse at the metadata level. We need better digital library services that allow users to discover and explore the content buried in these long documents. Recent advances in machine learning have shown promising results for decomposing documents into their constituent parts, but these models and techniques require data for training and evaluation. In this article, we present high-quality datasets to train, evaluate, and compare machine learning methods in tasks that are specifically suited to identify and extract key elements of ETD documents. We explain how we construct the datasets by manual labeling the data or by deriving labeled data through synthetic processes. We demonstrate how our datasets can be used to develop downstream applications and to evaluate, retrain, or fine-tune pre-trained machine learning models. We describe our ongoing work to compile benchmark datasets and exploit machine learning techniques to build intelligent digital libraries for ETDs.

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建立数据集,支持从电子论文中提取信息和解析结构
尽管网上公开的电子论文(ETD)多达数百万篇,但数字图书馆为电子论文提供的服务还没有超越元数据层面的简单搜索和浏览。我们需要更好的数字图书馆服务,让用户能够发现和探索这些长篇文献中隐藏的内容。机器学习领域的最新进展表明,将文档分解为各个组成部分的结果很有希望,但这些模型和技术需要数据来进行训练和评估。在本文中,我们提出了高质量的数据集,用于训练、评估和比较机器学习方法,这些方法特别适用于识别和提取 ETD 文档的关键要素。我们解释了如何通过人工标注数据或通过合成过程获得标注数据来构建数据集。我们展示了如何利用我们的数据集开发下游应用,以及如何评估、重新训练或微调预训练的机器学习模型。我们将介绍我们正在开展的工作,即编译基准数据集和利用机器学习技术为电子文献建立智能数字图书馆。
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来源期刊
CiteScore
4.30
自引率
6.70%
发文量
20
期刊介绍: The International Journal on Digital Libraries (IJDL) examines the theory and practice of acquisition definition organization management preservation and dissemination of digital information via global networking. It covers all aspects of digital libraries (DLs) from large-scale heterogeneous data and information management & access to linking and connectivity to security privacy and policies to its application use and evaluation.The scope of IJDL includes but is not limited to: The FAIR principle and the digital libraries infrastructure Findable: Information access and retrieval; semantic search; data and information exploration; information navigation; smart indexing and searching; resource discovery Accessible: visualization and digital collections; user interfaces; interfaces for handicapped users; HCI and UX in DLs; Security and privacy in DLs; multimodal access Interoperable: metadata (definition management curation integration); syntactic and semantic interoperability; linked data Reusable: reproducibility; Open Science; sustainability profitability repeatability of research results; confidentiality and privacy issues in DLs Digital Library Architectures including heterogeneous and dynamic data management; data and repositories Acquisition of digital information: authoring environments for digital objects; digitization of traditional content Digital Archiving and Preservation Digital Preservation and curation Digital archiving Web Archiving Archiving and preservation Strategies AI for Digital Libraries Machine Learning for DLs Data Mining in DLs NLP for DLs Applications of Digital Libraries Digital Humanities Open Data and their reuse Scholarly DLs (incl. bibliometrics altmetrics) Epigraphy and Paleography Digital Museums Future trends in Digital Libraries Definition of DLs in a ubiquitous digital library world Datafication of digital collections Interaction and user experience (UX) in DLs Information visualization Collection understanding Privacy and security Multimodal user interfaces Accessibility (or "Access for users with disabilities") UX studies
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