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Full-Text Articles in Library and Information Science
Automatic Metadata Extraction Incorporating Visual Features From Scanned Electronic Theses And Dissertations, Muntabir Hasan Choudhury, Himarsha R. Jayanetti, Jian Wu, William A. Ingram, Edward A. Fox
Automatic Metadata Extraction Incorporating Visual Features From Scanned Electronic Theses And Dissertations, Muntabir Hasan Choudhury, Himarsha R. Jayanetti, Jian Wu, William A. Ingram, Edward A. Fox
Computer Science Faculty Publications
Electronic Theses and Dissertations (ETDs) contain domain knowledge that can be used for many digital library tasks, such as analyzing citation networks and predicting research trends. Automatic metadata extraction is important to build scalable digital library search engines. Most existing methods are designed for born-digital documents, so they often fail to extract metadata from scanned documents such as ETDs. Traditional sequence tagging methods mainly rely on text-based features. In this paper, we propose a conditional random field (CRF) model that combines text-based and visual features. To verify the robustness of our model, we extended an existing corpus and created a …
Large Scale Subject Category Classification Of Scholarly Papers With Deep Attentive Neural Networks, Bharath Kandimalla, Shaurya Rohatgi, Jian Wu, C. Lee Giles
Large Scale Subject Category Classification Of Scholarly Papers With Deep Attentive Neural Networks, Bharath Kandimalla, Shaurya Rohatgi, Jian Wu, C. Lee Giles
Computer Science Faculty Publications
Subject categories of scholarly papers generally refer to the knowledge domain(s) to which the papers belong, examples being computer science or physics. Subject category classification is a prerequisite for bibliometric studies, organizing scientific publications for domain knowledge extraction, and facilitating faceted searches for digital library search engines. Unfortunately, many academic papers do not have such information as part of their metadata. Most existing methods for solving this task focus on unsupervised learning that often relies on citation networks. However, a complete list of papers citing the current paper may not be readily available. In particular, new papers that have few …